diff --git a/src/main/java/org/dromara/easyai/tools/GMClustering.java b/src/main/java/org/dromara/easyai/tools/GMClustering.java
new file mode 100644
index 0000000..9982a72
--- /dev/null
+++ b/src/main/java/org/dromara/easyai/tools/GMClustering.java
@@ -0,0 +1,106 @@
+package org.dromara.easyai.tools;
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.entity.RGBNorm;
+
+
+/**
+ * @param
+ * @DATA
+ * @Author LiDaPeng
+ * @Description
+ */
+public class GMClustering extends MeanClustering {
+    private float regionSize;//单区域面积
+
+    public float getRegionSize() {
+        return regionSize;
+    }
+
+    public void setRegionSize(float regionSize) {
+        this.regionSize = regionSize;
+    }
+
+    public GMClustering(int speciesQuantity, int maxTimes) throws Exception {
+        super(speciesQuantity, maxTimes);
+    }
+
+    public int getProbabilityDensity(float[] feature) throws Exception {//获取簇id
+        float maxPower = 0;
+        int id = 0;
+        int index = 0;
+        for (RGBNorm rgbNorm : matrices) {
+            float power = rgbNorm.getGMProbability(feature);
+            if (power > maxPower) {
+                maxPower = power;
+                id = index;
+            }
+            index++;
+        }
+        return id;
+    }
+
+    @Override
+    public void start() throws Exception {
+        super.start();
+        for (RGBNorm rgbNorm : matrices) {//高斯系数初始化
+            rgbNorm.gm();
+        }
+        for (int i = 0; i < 50; i++) {
+            gmClustering();
+        }
+    }
+
+    public void insertParameter(Matrix matrix) throws Exception {
+        int y = matrix.getY();
+        int size = y / speciesQuantity;
+        for (int i = 0; i <= y - size; i += size) {
+            float[] feature = new float[size];
+            RGBNorm rgbNorm = new RGBNorm();
+            matrices.add(rgbNorm);
+            for (int j = i; j < i + size; j++) {
+                feature[j - i] = matrix.getNumber(0, j);
+            }
+            rgbNorm.insertFeature(feature);
+        }
+    }
+
+    private void clear() {
+        for (RGBNorm rgbNorm : matrices) {//高斯系数初始化
+            rgbNorm.clearRGB();
+        }
+    }
+
+    private void gmClustering() throws Exception {//进行gm聚类
+        clear();
+        for (float[] rgb : matrixList) {//遍历当前集合
+            float allProbability = 0;//全概率
+            float[] pro = new float[speciesQuantity];
+            for (int i = 0; i < speciesQuantity; i++) {
+                RGBNorm rgbNorm = matrices.get(i);
+                float probability = rgbNorm.getGMProbability(rgb);
+                //System.out.println("pro===" + probability);
+                allProbability = allProbability + probability;
+                pro[i] = probability;
+            }
+            //求每个簇的后验概率
+            for (int i = 0; i < speciesQuantity; i++) {
+                pro[i] = pro[i] / allProbability;
+            }
+            //判断概率最大的簇
+            int index = 0;
+            float max = 0;
+            for (int i = 0; i < speciesQuantity; i++) {
+                if (pro[i] > max) {
+                    max = pro[i];
+                    index = i;
+                }
+            }
+            //注入特征
+            matrices.get(index).setGmFeature(rgb, pro[index]);
+        }
+        for (RGBNorm rgbNorm : matrices) {//高斯系数初始化
+            rgbNorm.gm();
+        }
+    }
+}