diff --git a/75%25%.py b/75%25%.py
new file mode 100644
index 0000000..cd68ac0
--- /dev/null
+++ b/75%25%.py
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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+使用 75% 训练 / 25% 测试 的方式评估 SVM（输出 ACC & AUC）
+"""
+
+import pickle
+import numpy as np
+from pathlib import Path
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, roc_auc_score
+
+
+# ---------- 1. 数据路径 ----------
+PKL_PATH = Path(r"D:\Python\空心检测\pythonProject\feature_dataset.pkl")
+
+# ---------- 2. 读取特征 ----------
+def load_pkl_matrix(path: Path):
+    with open(path, "rb") as f:
+        data = pickle.load(f)
+    return data["matrix"], data["label"]
+
+X, y = load_pkl_matrix(PKL_PATH)
+y = y.ravel()                           # shape (N,)
+
+# ---------- 3. 75% / 25% 拆分 ----------
+X_train, X_test, y_train, y_test = train_test_split(
+    X, y, test_size=0.25, random_state=42, stratify=y, shuffle=True
+)
+
+# ---------- 4. 标准化 + SVM ----------
+scaler = StandardScaler().fit(X_train)
+X_train_std = scaler.transform(X_train)
+X_test_std  = scaler.transform(X_test)
+
+svm = SVC(
+    kernel="rbf",
+    C=10,
+    gamma="scale",
+    probability=True,
+    class_weight="balanced",
+    random_state=42,
+)
+svm.fit(X_train_std, y_train)
+
+# ---------- 5. 评估 ----------
+y_pred        = svm.predict(X_test_std)
+y_proba_pos   = svm.predict_proba(X_test_std)[:, list(svm.classes_).index(1)]
+
+acc = accuracy_score(y_test, y_pred)
+auc = roc_auc_score(y_test, y_proba_pos)
+
+print("\n========== 评估结果 ==========")
+print(f"样本总数: {len(y)}  |  训练: {len(y_train)}  测试: {len(y_test)}")
+print(f"ACC  = {acc:.4f}")
+print(f"AUC  = {auc:.4f}")