diff --git a/src/kde-heatmap.js b/src/kde-heatmap.js
index 9358dda..ef21d76 100644
--- a/src/kde-heatmap.js
+++ b/src/kde-heatmap.js
@@ -19,7 +19,7 @@ function kernelEpanechnikov(bandwidth) {
 }
 
 /**
- * 核密度估计器
+ * 核密度估计器 - 优化版本
  * @param {Function} kernel 核函数
  * @param {Array} range 范围[xmin, xmax]
  * @param {Number} samples 采样点数
@@ -29,23 +29,75 @@ 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]]);
+    const bandwidth = 0.8; // 从核函数中提取带宽
+    
+    // 预计算核函数值缓存（减少重复计算）
+    const kernelCache = new Map();
+    const maxDistance = Math.ceil(bandwidth * 2 / gridSize); // 最大影响范围
+    
+    for (let dx = -maxDistance; dx <= maxDistance; dx++) {
+      for (let dy = -maxDistance; dy <= maxDistance; dy++) {
+        const distance = Math.sqrt(dx * dx + dy * dy) * gridSize;
+        if (distance <= bandwidth * 2) {
+          kernelCache.set(`${dx},${dy}`, kernel([dx * gridSize, dy * gridSize]));
         }
-        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;
+    // 使用稀疏网格计算（只计算有数据点影响的区域）
+    const affectedGridPoints = new Set();
+    
+    // 第一步：找出所有受影响的网格点
+    data.forEach(point => {
+      const centerX = Math.round((point[0] - range[0]) / gridSize);
+      const centerY = Math.round((point[1] - range[0]) / gridSize);
+      
+      // 只考虑带宽范围内的网格点
+      for (let dx = -maxDistance; dx <= maxDistance; dx++) {
+        for (let dy = -maxDistance; dy <= maxDistance; dy++) {
+          const gridX = centerX + dx;
+          const gridY = centerY + dy;
+          if (gridX >= 0 && gridX < samples && gridY >= 0 && gridY < samples) {
+            affectedGridPoints.add(`${gridX},${gridY}`);
+          }
+        }
+      }
     });
 
+    // 第二步：只计算受影响的网格点
+    affectedGridPoints.forEach(gridKey => {
+      const [gridX, gridY] = gridKey.split(',').map(Number);
+      const x = range[0] + gridX * gridSize;
+      const y = range[0] + gridY * gridSize;
+      
+      let sum = 0;
+      let validPoints = 0;
+      
+      // 只考虑附近的点（空间分割优化）
+      data.forEach(point => {
+        const dx = (x - point[0]) / gridSize;
+        const dy = (y - point[1]) / gridSize;
+        const cacheKey = `${Math.round(dx)},${Math.round(dy)}`;
+        
+        if (kernelCache.has(cacheKey)) {
+          sum += kernelCache.get(cacheKey);
+          validPoints++;
+        }
+      });
+      
+      if (validPoints > 0) {
+        densityData.push([x, y, sum / data.length]);
+      }
+    });
+
+    // 归一化
+    if (densityData.length > 0) {
+      const maxDensity = Math.max(...densityData.map((d) => d[2]));
+      densityData.forEach((d) => {
+        if (maxDensity > 0) d[2] /= maxDensity;
+      });
+    }
+
     return densityData;
   };
 }
@@ -102,8 +154,11 @@ function getHeatColor(density) {
   }
 }
 
+// 添加缓存机制
+const heatmapCache = new Map();
+
 /**
- * 基于小程序Canvas API绘制核密度估计热力图
+ * 基于小程序Canvas API绘制核密度估计热力图 - 带缓存优化
  * @param {String} canvasId 画布ID
  * @param {Number} width 画布宽度
  * @param {Number} height 画布高度
@@ -134,10 +189,68 @@ export function drawKDEHeatmap(canvasId, width, height, points, options = {}) {
         return;
       }
 
+      // 生成缓存key（基于参数和数据点的哈希）
+      const cacheKey = `${bandwidth}-${gridSize}-${range.join(',')}-${points.length}-${JSON.stringify(points.slice(0, 10))}`;
+      
+      // 检查缓存
+      if (heatmapCache.has(cacheKey)) {
+        console.log('使用缓存的热力图数据');
+        const cachedDensityData = heatmapCache.get(cacheKey);
+        
+        // 直接使用缓存数据绘制
+        const cellWidth = width / gridSize;
+        const cellHeight = height / gridSize;
+        const xRange = range[1] - range[0];
+        const yRange = range[1] - range[0];
+        
+        cachedDensityData.forEach((point) => {
+          const [x, y, density] = point;
+          const normalizedX = (x - range[0]) / xRange;
+          const normalizedY = (y - range[0]) / yRange;
+          const canvasX = normalizedX * width;
+          const canvasY = normalizedY * height;
+          const color = getHeatColor(density);
+          
+          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;
+            
+            ctx.beginPath();
+            ctx.arc(canvasX, canvasY, 2.5, 0, 2 * Math.PI);
+            ctx.fill();
+          });
+        }
+        
+        ctx.draw(false, () => {
+          console.log("KDE热力图绘制完成（缓存）");
+          resolve();
+        });
+        return;
+      }
+
       // 计算核密度估计
       const kernel = kernelEpanechnikov(bandwidth);
       const kde = kernelDensityEstimator(kernel, range, gridSize);
       const densityData = kde(points);
+      
+      // 缓存结果（限制缓存大小）
+      if (heatmapCache.size > 10) {
+        const firstKey = heatmapCache.keys().next().value;
+        heatmapCache.delete(firstKey);
+      }
+      heatmapCache.set(cacheKey, densityData);
 
       // 计算网格大小
       const cellWidth = width / gridSize;
@@ -145,47 +258,50 @@ export function drawKDEHeatmap(canvasId, width, height, points, options = {}) {
       const xRange = range[1] - range[0];
       const yRange = range[1] - range[0];
 
-      // 绘制热力图网格
+      // 绘制热力图网格 - 批量绘制优化
+      const colorGroups = new Map();
+      
+      // 按颜色分组，减少setFillStyle调用
       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）
+        
+        if (!colorGroups.has(color)) {
+          colorGroups.set(color, []);
+        }
+        colorGroups.get(color).push(point);
+      });
+      
+      // 批量绘制相同颜色的点
+      colorGroups.forEach((points, color) => {
         ctx.setFillStyle(color);
-        ctx.beginPath();
-        ctx.arc(
-          canvasX,
-          canvasY,
-          Math.min(cellWidth, cellHeight) * 0.6,
-          0,
-          2 * Math.PI
-        );
-        ctx.fill();
+        points.forEach((point) => {
+          const [x, y, density] = point;
+          const normalizedX = (x - range[0]) / xRange;
+          const normalizedY = (y - range[0]) / yRange;
+          const canvasX = normalizedX * width;
+          const canvasY = normalizedY * height;
+          
+          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
+      // 绘制原始数据点 - 批量绘制优化
       if (showPoints) {
         ctx.setFillStyle(pointColor);
+        ctx.beginPath(); // 开始批量路径
         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();
         });
+        ctx.fill(); // 一次性填充所有圆点
       }
 
       // 执行绘制
@@ -235,6 +351,15 @@ export function generateKDEHeatmapImage(
   });
 }
 
+/**
+ * 清除热力图缓存
+ * 在数据或参数需要强制更新时调用
+ */
+export function clearHeatmapCache() {
+  heatmapCache.clear();
+  console.log('热力图缓存已清除');
+}
+
 export const generateHeatMapData = (width, height, amount = 100) => {
   const data = [];
   const centerX = 0.5; // 中心点X坐标