diff --git a/Graph/goal_position_probability.png b/Graph/goal_position_probability.png
index 799c14cfe330cef5884bd9fa555ec599bc148238..3253caacb96f5d7d4176f72af745956ba65f7600 100644
Binary files a/Graph/goal_position_probability.png and b/Graph/goal_position_probability.png differ
diff --git a/Graph/goal_prob_by_manpowersituation.png b/Graph/goal_prob_by_manpowersituation.png
index 3d6b660a746e16ab1661d0a06d7bd28239f2971d..5c0a24d4cad22ed3113ebfef4faacf29b8980c94 100644
Binary files a/Graph/goal_prob_by_manpowersituation.png and b/Graph/goal_prob_by_manpowersituation.png differ
diff --git a/Graph/goal_prob_by_scorediff.png b/Graph/goal_prob_by_scorediff.png
index 61c0247c44a2e4cc5a06edb1f4277a198bfbc465..fc1dd4fb06aa0dc1586d8480df05b1e2dbac9e3b 100644
Binary files a/Graph/goal_prob_by_scorediff.png and b/Graph/goal_prob_by_scorediff.png differ
diff --git a/Graph/xgb_feature_importance.png b/Graph/xgb_feature_importance.png
index 4257a513083d759dd69a9e0f6de52382fa8edb7a..1452e4846d0ac47a20f222532905ee0ad8a54862 100644
Binary files a/Graph/xgb_feature_importance.png and b/Graph/xgb_feature_importance.png differ
diff --git a/Scripts/main_XGB.py b/Scripts/main_XGB.py
index a2df3592ff6fa59a33eb9ae8e16554cf5344e964..e264e59ee7d26ab8ef258cbe9af5870fb3ba7441 100644
--- a/Scripts/main_XGB.py
+++ b/Scripts/main_XGB.py
@@ -61,15 +61,15 @@ print(classification_report(y_test, y_pred))
 print("=== 混淆矩阵 ===")
 print(confusion_matrix(y_test, y_pred))
 
-# === 9. 特征重要性可视化并保存 ===
+
 plt.figure(figsize=(12, 8))
 xgb.plot_importance(model, height=0.6, max_num_features=20, importance_type='gain')
-plt.title("XGBoost 特征重要性（按信息增益）")
+plt.title("XGBoost Feature Importance (by Gain)")  # 英文标题
 plt.tight_layout()
 
-# 👉 保存图像到模型目录
+
 importance_plot_path = os.path.join("../Graph", "xgb_feature_importance.png")
 plt.savefig(importance_plot_path, dpi=300, bbox_inches='tight')
-print(f"📊 特征重要性图已保存到: {importance_plot_path}")
+print(f"📊 Feature importance plot saved to: {importance_plot_path}")
 
 plt.show()
diff --git a/Scripts/main_XGB2.py b/Scripts/main_XGB2.py
index e64783ebbeb616dd8deda17246cae2d7a3681363..5f3d0e5c27e675cedefdd6a6094c75826b1e80bf 100644
--- a/Scripts/main_XGB2.py
+++ b/Scripts/main_XGB2.py
@@ -70,37 +70,38 @@ df_test['scoredifferential'] = df.loc[df_test.index, 'scoredifferential']
 # 10. 进球位置与进球概率关系图
 plt.figure(figsize=(10, 6))
 plt.scatter(df_test['xadjcoord'], df_test['yadjcoord'], c=df_test['goal_prob'], cmap='coolwarm', alpha=0.5)
-plt.colorbar(label="进球概率")
-plt.title("进球位置与进球概率的关系")
+plt.colorbar(label="Goal Probability")
+plt.title("Relationship Between Goal Location and Goal Probability")
 plt.xlabel("xadjcoord")
 plt.ylabel("yadjcoord")
 plt.axhline(0, color='gray', linestyle='--')
 plt.axvline(0, color='gray', linestyle='--')
-
-# ✅ 保存图像
 plt.tight_layout()
 plt.savefig("../Graph/goal_position_probability.png", dpi=300, bbox_inches='tight')
 plt.show()
 
+
+
 # 11. 局势 vs 进球概率
 plt.figure(figsize=(12, 6))
 sns.boxplot(x=df_test['manpowersituation'], y=df_test['goal_prob'])
-plt.title("不同局势下的进球概率")
-plt.xlabel("比赛局势")
-plt.ylabel("进球概率")
+plt.title("Goal Probability Across Different Manpower Situations")
+plt.xlabel("Manpower Situation")
+plt.ylabel("Goal Probability")
 plt.xticks(rotation=45)
 plt.tight_layout()
-plt.savefig("../Graph/goal_prob_by_manpowersituation.png", dpi=300, bbox_inches='tight')  # ✅ 保存
+plt.savefig("../Graph/goal_prob_by_manpowersituation.png", dpi=300, bbox_inches='tight')
 plt.show()
 
 
 # 12. 比分差 vs 进球概率
 plt.figure(figsize=(10, 6))
 sns.lineplot(x=df_test['scoredifferential'], y=df_test['goal_prob'], marker='o')
-plt.title("比分差与进球概率的关系")
-plt.xlabel("比分差（本队 - 对方）")
-plt.ylabel("进球概率")
+plt.title("Goal Probability vs Score Differential")
+plt.xlabel("Score Differential (Team - Opponent)")
+plt.ylabel("Goal Probability")
 plt.tight_layout()
-plt.savefig("../Graph/goal_prob_by_scorediff.png", dpi=300, bbox_inches='tight')  # ✅ 保存
+plt.savefig("../Graph/goal_prob_by_scorediff.png", dpi=300, bbox_inches='tight')
 plt.show()
 
+
diff --git a/Scripts/process_1.py b/Scripts/process_1.py
index 929c1163c750f180ddc4a80896c79dca5df6610c..84c1e8de038cbb27de6b38bf14a0f8c9c90609a4 100644
--- a/Scripts/process_1.py
+++ b/Scripts/process_1.py
@@ -10,41 +10,29 @@ df = pd.read_csv("Linhac24-25_Sportlogiq.csv")
 print(df.info())
 print(df.head())
 
-# =============================
-# 1. 各类事件统计
-# =============================
+
 event_counts = df['eventname'].value_counts()
 print("事件类型统计:\n", event_counts)
 
-# =============================
-# 2. 分析控球球队的控球次数
-# =============================
+
 team_possession_counts = df['teaminpossession'].value_counts()
 print("控球队出现次数:\n", team_possession_counts)
 
-# =============================
-# 3. xG（expected goals）分析
-# =============================
+
 df['xg_allattempts'] = pd.to_numeric(df['xg_allattempts'], errors='coerce')
 xg_by_team = df.groupby('teamid')['xg_allattempts'].sum()
 print("每支球队的总xG:\n", xg_by_team)
 
-# =============================
-# 4. 球员事件参与统计
-# =============================
+
 player_actions = df['playerid'].value_counts().head(10)
 print("参与最多事件的前10位球员:\n", player_actions)
 
 
-# =============================
-# ５. 成功 vs 失败 事件比例
-# =============================
+
 success_rate = df['outcome'].value_counts(normalize=True)
 print("事件成功与失败比例:\n", success_rate)
 
-# =============================
-# ６. 替你做一个简单总结
-# =============================
+
 print("\n简单总结：")
 print(f"总事件数: {len(df)}")
 print(f"总xG: {df['xg_allattempts'].sum():.2f}")
diff --git a/Scripts/process_2.py b/Scripts/process_2.py
index 8a2faba621b2f8353452ff18634ddd779cf7fdea..c5018e18c8967cbf8e777aed901baa9446631f54 100644
--- a/Scripts/process_2.py
+++ b/Scripts/process_2.py
@@ -3,22 +3,18 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 import matplotlib
 
-# 设置支持中文的字体
 matplotlib.rcParams['font.family'] = 'Microsoft YaHei'  # 设置中文字体为微软雅黑
 matplotlib.rcParams['axes.unicode_minus'] = False       # 正确显示负号
-# 加载预处理后的数据
+
 df = pd.read_csv("Linhac24-25_Sportlogiq.csv")
 
-# 设置图形风格
 sns.set(style="whitegrid")
 
-# 1. 数据基本信息
 print("📌 数据基本信息：")
 print(df.info())
 print("\n📈 描述性统计：")
 print(df.describe())
 
-# 2. 类别分布（事件类型）
 print("\n📊 不同事件类型分布：")
 print(df['eventname'].value_counts())
 
@@ -29,7 +25,7 @@ plt.title("事件类型分布")
 plt.tight_layout()
 plt.show()
 
-# 3. xG 分布分析
+
 plt.figure(figsize=(8, 4))
 sns.histplot(df['xg_allattempts'], bins=30, kde=True)
 plt.title("xG 分布")
@@ -37,7 +33,7 @@ plt.xlabel("xG 值")
 plt.tight_layout()
 plt.show()
 
-# 4. 相关性热力图（数值型特征）
+
 corr = df[['compiledgametime', 'xadjcoord', 'yadjcoord', 'xg_allattempts']].corr()
 plt.figure(figsize=(6, 4))
 sns.heatmap(corr, annot=True, cmap='coolwarm')
@@ -45,7 +41,7 @@ plt.title("数值型字段相关性")
 plt.tight_layout()
 plt.show()
 
-# 5. 时间分布：每分钟事件数量
+
 df['event_minute'] = df['compiledgametime'] // 60
 minute_event_count = df.groupby('event_minute').size()