diff --git a/src/main/java/org/dromara/easyai/entity/RGBNorm.java b/src/main/java/org/dromara/easyai/entity/RGBNorm.java
new file mode 100644
index 0000000..78ce534
--- /dev/null
+++ b/src/main/java/org/dromara/easyai/entity/RGBNorm.java
@@ -0,0 +1,302 @@
+package org.dromara.easyai.entity;
+
+import org.dromara.easyai.matrixTools.Matrix;
+import org.dromara.easyai.tools.RgbRegression;
+
+import java.util.*;
+
+public class RGBNorm {
+    private float[] rgbAll;
+    private float norm;
+    private int nub;
+    private float[] rgb;//均值
+    private float[] rgbUp;
+    private List<float[]> rgbs = new ArrayList<>();//需要对它进行排序
+    private List<Float> powers = new ArrayList<>();//需要对它进行排序
+    private RgbRegression rgbRegression;
+    private int len;
+    private float varAll = 1;
+    private Matrix avgMatrix;//均值矩阵
+    private Matrix varMatrix;//方差矩阵
+    private float gmParameter;//gm混合系数
+    private float probabilitySigma = 0;//后验概率求和
+
+    public float getVarAll() {
+        return varAll;
+    }
+
+    public Matrix getAvgMatrix() {
+        return avgMatrix;
+    }
+
+    public Matrix getVarMatrix() {
+        return varMatrix;
+    }
+
+    public List<float[]> getRgbs() {
+        return rgbs;
+    }
+
+    public RgbRegression getRgbRegression() {
+        return rgbRegression;
+    }
+
+    public void setRgbRegression(RgbRegression rgbRegression) {
+        this.rgbRegression = rgbRegression;
+    }
+
+    public RGBNorm(float[] rgb, int len) {
+        this.len = len;
+        rgbAll = new float[len];
+        this.rgb = new float[len];
+        this.rgbUp = rgb;
+        gmParameter = new Random().nextFloat();
+    }
+
+    public RGBNorm() {
+    }
+
+    public void syn() {
+        rgbUp = rgb;
+    }
+
+    public void clearRGB() {
+        probabilitySigma = 0;
+        rgbs.clear();
+        powers.clear();
+    }
+
+    //特征及权重
+    public void setGmFeature(float[] feature, float probability) {
+        rgbs.add(feature);
+        powers.add(probability);
+        probabilitySigma = probability + probabilitySigma;
+    }
+
+    public void clear() {
+        rgbAll = new float[len];
+        nub = 0;
+        for (int i = 0; i < rgb.length; i++) {
+            rgbUp[i] = rgb[i];
+        }
+        rgbs.clear();
+        //System.out.println("clear==" + Arrays.toString(rgbUp));
+    }
+
+    public int getNub() {
+        return nub;
+    }
+
+    public boolean compare() {
+        boolean isNext = false;
+        for (int i = 0; i < rgb.length; i++) {
+            if (rgb[i] != rgbUp[i]) {
+                isNext = true;
+                break;
+            }
+        }
+        return isNext;
+    }
+
+    public float getEDist(float[] x) {
+        float[] y = new float[x.length];
+        for (int i = 0; i < y.length; i++) {
+            y[i] = x[i] - rgbUp[i];
+        }
+        float sigma = 0;
+        for (int i = 0; i < y.length; i++) {
+            sigma = sigma + (float)Math.pow(y[i], 2);
+        }
+        return (float)Math.sqrt(sigma);
+    }
+
+    public void setColor(float[] rgb) {
+        for (int i = 0; i < rgb.length; i++) {
+            rgbAll[i] = rgbAll[i] + rgb[i];
+        }
+        rgbs.add(rgb);
+        nub++;
+    }
+
+    private Matrix average(Matrix matrix) throws Exception {//计算平均值
+        int x = matrix.getX();
+        int y = matrix.getY();
+        Matrix avgMatrix = new Matrix(x, 1);
+        boolean isPower = powers.size() > 1;
+        for (int i = 0; i < x; i++) {
+            for (int j = 0; j < y; j++) {
+                float power;
+                if (!isPower) {
+                    power = 1;
+                } else {
+                    power = powers.get(j);
+                }
+                float nub = avgMatrix.getNumber(i, 0) + matrix.getNumber(i, j) * power;
+                avgMatrix.setNub(i, 0, nub);
+            }
+        }
+        for (int i = 0; i < x; i++) {
+            float nub;
+            if (probabilitySigma > 0) {
+                nub = avgMatrix.getNumber(i, 0) / probabilitySigma;
+            } else {
+                nub = avgMatrix.getNumber(i, 0) / y;
+            }
+            avgMatrix.setNub(i, 0, nub);
+        }
+        return avgMatrix;
+    }
+
+    private Matrix getVariance(Matrix matrix, Matrix avgMatrix) throws Exception {
+        int x = matrix.getX();
+        int y = matrix.getY();
+        Matrix varMatrix = new Matrix(x, 1);
+        boolean isPower = powers.size() > 1;
+        for (int i = 0; i < x; i++) {
+            float avg = avgMatrix.getNumber(i, 0);
+            for (int j = 0; j < y; j++) {
+                float power;
+                if (!isPower) {
+                    power = 1;
+                } else {
+                    power = powers.get(j);
+                }
+                float sub = (float) ((float)Math.pow(matrix.getNumber(i, j) - avg, 2) * power);
+                varMatrix.setNub(i, 0, sub + varMatrix.getNumber(i, 0));
+            }
+        }
+        for (int i = 0; i < x; i++) {
+            float nub;
+            if (probabilitySigma > 0) {
+                nub = varMatrix.getNumber(i, 0) / probabilitySigma;
+            } else {
+                nub = varMatrix.getNumber(i, 0) / y;
+            }
+            varMatrix.setNub(i, 0, nub);
+        }
+        return varMatrix;
+    }
+
+    public float[] getFeature() throws Exception {
+        int avgLen = avgMatrix.getX();
+        int length = avgLen * 2 + 1;
+        float[] feature = new float[length];
+        for (int i = 0; i < length - 1; i++) {
+            if (i < avgLen) {
+                feature[i] = avgMatrix.getNumber(i, 0);
+            } else {
+                int t = i - avgLen;
+                feature[i] = varMatrix.getNumber(t, 0);
+            }
+        }
+        feature[length - 1] = gmParameter;
+        return feature;
+    }
+
+    public void insertFeature(float[] feature) throws Exception {
+        int length = feature.length - 1;
+        int size = length / 2;
+        avgMatrix = new Matrix(size, 1);
+        varMatrix = new Matrix(size, 1);
+        gmParameter = feature[length];
+        varAll = 1;
+        for (int i = 0; i < length; i++) {
+            if (i < size) {
+                avgMatrix.setNub(i, 0, feature[i]);
+            } else {
+                int t = i - size;
+                float var = feature[i];
+                varMatrix.setNub(t, 0, var);
+                varAll = varAll * (float)Math.sqrt(var);
+            }
+        }
+    }
+
+    public void gm() throws Exception {//混合高斯聚类模型参数计算
+        int size = rgbs.size();
+        if (size > 0) {
+            Matrix matrix = new Matrix(len, size);
+            for (int i = 0; i < size; i++) {
+                float[] rgb = rgbs.get(i);
+                for (int j = 0; j < len; j++) {
+                    matrix.setNub(j, i, rgb[j]);
+                }
+            }
+            avgMatrix = average(matrix);//均值矩阵
+            varMatrix = getVariance(matrix, avgMatrix);//方差矩阵
+            varAll = 1;
+            for (int i = 0; i < len; i++) {
+                float var = (float)Math.sqrt(varMatrix.getNumber(i, 0));
+                varAll = varAll * var;
+            }
+            //确认新的混合系数
+            if (probabilitySigma > 0) {
+                gmParameter = probabilitySigma / rgbs.size();
+            }
+        }
+    }
+
+    public float getGMProbability(float[] feature) throws Exception {//计算正态分布概率密度
+        float zSigma = 0;
+        int size = feature.length;
+        for (int i = 0; i < size; i++) {
+            float sub = (float)Math.pow(feature[i] - avgMatrix.getNumber(i, 0), 2) / varMatrix.getNumber(i, 0);
+            zSigma = sub + zSigma;
+        }
+        float one = 1 / ((float)Math.pow((float)Math.sqrt((float)Math.PI * 2), size) * varAll);
+        float two = (float)Math.exp(zSigma * -0.5);
+        float n = one * two * gmParameter;
+        return n;
+    }
+
+    public float getGmParameter() {
+        return gmParameter;
+    }
+
+    public void setGmParameter(float gmParameter) {
+        this.gmParameter = gmParameter;
+    }
+
+    public void norm() {//范长计算
+        float sigma = 0;
+        if (nub > 0) {
+            for (int i = 0; i < rgb.length; i++) {
+                float rgbc = rgbAll[i] / nub;
+                rgb[i] = rgbc;
+                sigma = sigma + (float)Math.pow(rgbc, 2);
+            }
+            norm = (float)Math.sqrt(sigma);
+        }
+    }
+
+    public void finish() {//进行排序
+        RGBListSort rgbListSort = new RGBListSort();
+        Collections.sort(rgbs, rgbListSort);
+    }
+
+    public float getNorm() {
+        return norm;
+    }
+
+    public float[] getRgb() {
+        return rgb;
+    }
+
+    class RGBListSort implements Comparator<float[]> {
+        @Override
+        public int compare(float[] o1, float[] o2) {
+            float o1Norm = 0;
+            float o2Norm = 0;
+            for (int i = 0; i < o1.length; i++) {
+                o1Norm = o1Norm + (float)Math.pow(o1[i], 2);
+                o2Norm = o2Norm + (float)Math.pow(o2[i], 2);
+            }
+            if (o1Norm > o2Norm) {
+                return 1;
+            } else if (o1Norm < o2Norm) {
+                return -1;
+            }
+            return 0;
+        }
+    }
+}