diff --git a/src/App.vue b/src/App.vue
index 0d983d5..309f5e7 100644
--- a/src/App.vue
+++ b/src/App.vue
@@ -64,6 +64,12 @@ button {
   box-sizing: border-box;
 }
 
+view::-webkit-scrollbar {
+  width: 0;
+  height: 0;
+  color: transparent;
+}
+
 button::after {
   border: none;
 }
diff --git a/src/kde-heatmap.js b/src/kde-heatmap.js
new file mode 100644
index 0000000..9358dda
--- /dev/null
+++ b/src/kde-heatmap.js
@@ -0,0 +1,270 @@
+/**
+ * 基于小程序Canvas API的核密度估计热力图
+ * 实现类似test.html中的效果，但适配uni-app小程序环境
+ */
+
+/**
+ * Epanechnikov核函数
+ * @param {Number} bandwidth 带宽参数
+ * @returns {Function} 核函数
+ */
+function kernelEpanechnikov(bandwidth) {
+  return function (v) {
+    const r = Math.sqrt(v[0] * v[0] + v[1] * v[1]);
+    return r <= bandwidth
+      ? (3 / (Math.PI * bandwidth * bandwidth)) *
+          (1 - (r * r) / (bandwidth * bandwidth))
+      : 0;
+  };
+}
+
+/**
+ * 核密度估计器
+ * @param {Function} kernel 核函数
+ * @param {Array} range 范围[xmin, xmax]
+ * @param {Number} samples 采样点数
+ * @returns {Function} 密度估计函数
+ */
+function kernelDensityEstimator(kernel, range, samples) {
+  return function (data) {
+    const gridSize = (range[1] - range[0]) / samples;
+    const densityData = [];
+
+    for (let x = range[0]; x <= range[1]; x += gridSize) {
+      for (let y = range[0]; y <= range[1]; y += gridSize) {
+        let sum = 0;
+        for (const point of data) {
+          sum += kernel([x - point[0], y - point[1]]);
+        }
+        densityData.push([x, y, sum / data.length]);
+      }
+    }
+
+    // 归一化
+    const maxDensity = Math.max(...densityData.map((d) => d[2]));
+    densityData.forEach((d) => {
+      if (maxDensity > 0) d[2] /= maxDensity;
+    });
+
+    return densityData;
+  };
+}
+
+/**
+ * 生成随机射箭数据点
+ * @param {Number} centerCount 中心点数量
+ * @param {Number} pointsPerCenter 每个中心点的箭数
+ * @returns {Array} 箭矢坐标数组
+ */
+export function generateArcheryPoints(centerCount = 2, pointsPerCenter = 100) {
+  const points = [];
+  const range = 8; // 坐标范围 -4 到 4
+  const spread = 3; // 分散度
+
+  for (let i = 0; i < centerCount; i++) {
+    const centerX = Math.random() * range - range / 2;
+    const centerY = Math.random() * range - range / 2;
+
+    for (let j = 0; j < pointsPerCenter; j++) {
+      points.push([
+        centerX + (Math.random() - 0.5) * spread,
+        centerY + (Math.random() - 0.5) * spread,
+      ]);
+    }
+  }
+
+  return points;
+}
+
+/**
+ * 颜色映射函数 - 将密度值映射到颜色
+ * @param {Number} density 密度值 0-1
+ * @returns {String} RGBA颜色字符串
+ */
+function getHeatColor(density) {
+  // 绿色系热力图：从浅绿到深绿
+  if (density < 0.1) return "rgba(0, 255, 0, 0)";
+
+  const alpha = Math.min(density * 1.2, 1); // 增强透明度
+  const intensity = density;
+
+  if (intensity < 0.5) {
+    // 低密度：浅绿色
+    const green = Math.round(200 + 55 * intensity);
+    const blue = Math.round(50 + 100 * intensity);
+    return `rgba(${Math.round(50 * intensity)}, ${green}, ${blue}, ${alpha * 0.7})`;
+  } else {
+    // 高密度：深绿色
+    const red = Math.round(50 * (intensity - 0.5) * 2);
+    const green = Math.round(180 + 75 * (1 - intensity));
+    const blue = Math.round(30 * (1 - intensity));
+    return `rgba(${red}, ${green}, ${blue}, ${alpha * 0.7})`;
+  }
+}
+
+/**
+ * 基于小程序Canvas API绘制核密度估计热力图
+ * @param {String} canvasId 画布ID
+ * @param {Number} width 画布宽度
+ * @param {Number} height 画布高度
+ * @param {Array} points 箭矢坐标数组 [[x, y], ...]
+ * @param {Object} options 可选参数
+ * @returns {Promise} 绘制完成的Promise
+ */
+export function drawKDEHeatmap(canvasId, width, height, points, options = {}) {
+  return new Promise((resolve, reject) => {
+    try {
+      const {
+        bandwidth = 0.8,
+        gridSize = 100,
+        range = [-4, 4],
+        showPoints = true,
+        pointColor = "rgba(255, 255, 255, 0.9)",
+      } = options;
+
+      // 创建绘图上下文
+      const ctx = uni.createCanvasContext(canvasId);
+
+      // 清空画布
+      ctx.clearRect(0, 0, width, height);
+
+      // 如果没有数据，直接绘制
+      if (!points || points.length === 0) {
+        ctx.draw(false, () => resolve());
+        return;
+      }
+
+      // 计算核密度估计
+      const kernel = kernelEpanechnikov(bandwidth);
+      const kde = kernelDensityEstimator(kernel, range, gridSize);
+      const densityData = kde(points);
+
+      // 计算网格大小
+      const cellWidth = width / gridSize;
+      const cellHeight = height / gridSize;
+      const xRange = range[1] - range[0];
+      const yRange = range[1] - range[0];
+
+      // 绘制热力图网格
+      densityData.forEach((point) => {
+        const [x, y, density] = point;
+
+        // 将逻辑坐标转换为画布坐标
+        const normalizedX = (x - range[0]) / xRange;
+        const normalizedY = (y - range[0]) / yRange;
+        const canvasX = normalizedX * width;
+        const canvasY = normalizedY * height;
+
+        // 获取颜色
+        const color = getHeatColor(density);
+
+        // 绘制单元格（使用圆形绘制，边缘更平滑）
+        ctx.setFillStyle(color);
+        ctx.beginPath();
+        ctx.arc(
+          canvasX,
+          canvasY,
+          Math.min(cellWidth, cellHeight) * 0.6,
+          0,
+          2 * Math.PI
+        );
+        ctx.fill();
+      });
+
+      // 绘制原始数据点
+      if (showPoints) {
+        ctx.setFillStyle(pointColor);
+        points.forEach((point) => {
+          const [x, y] = point;
+          const normalizedX = (x - range[0]) / xRange;
+          const normalizedY = (y - range[0]) / yRange;
+          const canvasX = normalizedX * width;
+          const canvasY = normalizedY * height;
+
+          // 绘制小圆点
+          ctx.beginPath();
+          ctx.arc(canvasX, canvasY, 2.5, 0, 2 * Math.PI);
+          ctx.fill();
+        });
+      }
+
+      // 执行绘制
+      ctx.draw(false, () => {
+        console.log("KDE热力图绘制完成");
+        resolve();
+      });
+    } catch (error) {
+      console.error("KDE热力图绘制失败:", error);
+      reject(error);
+    }
+  });
+}
+
+/**
+ * 生成热力图图片（类似原有的generateHeatmapImage函数）
+ * 但使用核密度估计算法
+ */
+export function generateKDEHeatmapImage(
+  canvasId,
+  width,
+  height,
+  points,
+  options = {}
+) {
+  return new Promise((resolve, reject) => {
+    drawKDEHeatmap(canvasId, width, height, points, options)
+      .then(() => {
+        // 生成图片
+        uni.canvasToTempFilePath({
+          canvasId: canvasId,
+          width: width,
+          height: height,
+          destWidth: width * 3, // 提高输出分辨率，让图像更细腻
+          destHeight: height * 3,
+          success: (res) => {
+            console.log("KDE热力图图片生成成功:", res.tempFilePath);
+            resolve(res.tempFilePath);
+          },
+          fail: (error) => {
+            console.error("KDE热力图图片生成失败:", error);
+            reject(error);
+          },
+        });
+      })
+      .catch(reject);
+  });
+}
+
+export const generateHeatMapData = (width, height, amount = 100) => {
+  const data = [];
+  const centerX = 0.5; // 中心点X坐标
+  const centerY = 0.5; // 中心点Y坐标
+
+  for (let i = 0; i < amount; i++) {
+    let x, y;
+
+    // 30%的数据集中在中心区域（高斯分布）
+    if (Math.random() < 0.3) {
+      // 使用正态分布生成中心区域的数据
+      const angle = Math.random() * 2 * Math.PI;
+      const radius = Math.sqrt(-2 * Math.log(Math.random())) * 0.15; // 标准差0.15
+      x = centerX + radius * Math.cos(angle);
+      y = centerY + radius * Math.sin(angle);
+    } else {
+      x = Math.random() * 0.8 + 0.1; // 0.1-0.9范围
+      y = Math.random() * 0.8 + 0.1;
+    }
+
+    // 确保坐标在0-1范围内
+    x = Math.max(0.05, Math.min(0.95, x));
+    y = Math.max(0.05, Math.min(0.95, y));
+
+    data.push({
+      x: parseFloat(x.toFixed(3)),
+      y: parseFloat(y.toFixed(3)),
+      ring: Math.floor(Math.random() * 5) + 6, // 6-10环
+    });
+  }
+
+  return data;
+};
diff --git a/src/pages/point-book.vue b/src/pages/point-book.vue
index 0e042d1..e83b825 100644
--- a/src/pages/point-book.vue
+++ b/src/pages/point-book.vue
@@ -17,7 +17,8 @@ import {
 } from "@/apis";
 
 import { getElementRect } from "@/util";
-import { generateHeatmapImage } from "@/heatmap";
+
+import { generateKDEHeatmapImage } from "@/kde-heatmap";
 
 import useStore from "@/store";
 import { storeToRefs } from "pinia";
@@ -61,75 +62,39 @@ const startScoring = () => {
   }
 };
 
-// 生成热力图测试数据
-const generateHeatMapData = (width, height, amount = 100) => {
-  const data = [];
-  const centerX = 0.5; // 中心点X坐标
-  const centerY = 0.5; // 中心点Y坐标
-
-  // 生成500条记录
-  for (let i = 0; i < amount; i++) {
-    let x, y, count;
-
-    // 30%的数据集中在中心区域（高斯分布）
-    if (Math.random() < 0.3) {
-      // 使用正态分布生成中心区域的数据
-      const angle = Math.random() * 2 * Math.PI;
-      const radius = Math.sqrt(-2 * Math.log(Math.random())) * 0.15; // 标准差0.15
-      x = centerX + radius * Math.cos(angle);
-      y = centerY + radius * Math.sin(angle);
-      count = Math.floor(Math.random() * 20);
-    } else {
-      x = Math.random() * 0.8 + 0.1; // 0.1-0.9范围
-      y = Math.random() * 0.8 + 0.1;
-      count = Math.floor(Math.random() * 20);
-    }
-
-    // 确保坐标在0-1范围内
-    x = Math.max(0.05, Math.min(0.95, x));
-    y = Math.max(0.05, Math.min(0.95, y));
-
-    data.push({
-      x: parseFloat(x.toFixed(3)),
-      y: parseFloat(y.toFixed(3)),
-      ring: Math.floor(Math.random() * 5) + 6, // 6-10环
-      count: count,
-    });
-  }
-
-  return data;
-};
-
 const loadData = async () => {
   const result = await getPointBookListAPI(1);
   list.value = result.slice(0, 3);
   const result2 = await getPointBookStatisticsAPI();
+  data.value = result2;
 
   const rect = await getElementRect(".heat-map");
-  // const testWeekArrows = generateHeatMapData(rect.width, rect.height);
-  // result2.weekArrows = testWeekArrows;
-  // console.log(result2.weekArrows)
-  data.value = result2;
   let hot = 0;
   if (result2.checkInCount > -3 && result2.checkInCount < 3) hot = 1;
   else if (result2.checkInCount >= 3) hot = 2;
   else if (result2.checkInCount >= 5) hot = 3;
   else if (result2.checkInCount === 7) hot = 4;
   uni.$emit("update-hot", hot);
-  setTimeout(async () => {
-    try {
-      const imagePath = await generateHeatmapImage(
-        "heatMapCanvas",
-        rect.width,
-        rect.height,
-        result2.weekArrows.filter((item) => item.x && item.y)
-      );
-      heatMapImageSrc.value = imagePath; // 存储生成的图片地址
-      console.log("热力图图片地址:", imagePath);
-    } catch (error) {
-      console.error("生成热力图图片失败:", error);
-    }
-  }, 500);
+  try {
+    const imagePath = await generateKDEHeatmapImage(
+      "heatMapCanvas",
+      rect.width,
+      rect.height,
+      result2.weekArrows
+        .filter((item) => item.x && item.y)
+        .map((item) => [item.x, item.y]),
+      {
+        range: [0, 1], // 适配0-1坐标范围
+        gridSize: 150, // 更高的网格密度，减少锯齿
+        bandwidth: 0.15, // 稍小的带宽，让热力图更细腻
+        showPoints: true, // 显示白色原始数据点
+      }
+    );
+    heatMapImageSrc.value = imagePath; // 存储生成的图片地址
+    console.log("热力图图片地址:", imagePath);
+  } catch (error) {
+    console.error("生成热力图图片失败:", error);
+  }
 };
 
 watch(