kw_tools/SEO/pitchures_2.py

import networkx as nx
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
import os
import requests

# 中文字體設定（自動下載 Google Noto Sans TC）
font_url = "https://github.com/googlefonts/noto-cjk/raw/main/Sans/OTF/TraditionalChinese/NotoSansTC-Regular.otf"
font_path = "NotoSansTC-Regular.otf"
if not os.path.exists(font_path):
    r = requests.get(font_url)
    with open(font_path, "wb") as f:
        f.write(r.content)
font_prop = fm.FontProperties(fname=font_path)
plt.rcParams['font.family'] = font_prop.get_name()

# 關鍵字關聯資料
edge_list = [
    ('血液分離過濾器', '雙重過濾血漿分離術價格'),
    ('血液分離過濾器', '血漿置換術費用'),
    ('血液分離過濾器', '血液淨化療程費用'),
    ('血液分離過濾器', '血漿分離術費用'),
    ('血液分離過濾器', '血漿置換術後遺症'),
    ('血液分離過濾器', '雙重過濾血漿分離術健保'),
    ('血液分離過濾器', '血液淨化副作用'),
    ('雙重過濾血漿分離術自費', '血漿置換術自費'),
    ('雙重過濾血漿分離術自費', '血漿置換術費用'),
    ('雙重過濾血漿分離術自費', '雙重過濾血漿分離術健保'),
    # 你可以貼上完整 edge_list 在這裡
]

# 建立網絡圖
G = nx.Graph()
G.add_edges_from(edge_list)

# 繪製圖形
plt.figure(figsize=(12, 10))
pos = nx.spring_layout(G, k=0.5, seed=42)
nx.draw_networkx_nodes(G, pos, node_color='#90CAF9', node_size=1200)
nx.draw_networkx_edges(G, pos, edge_color='#B0BEC5')
nx.draw_networkx_labels(G, pos, font_properties=font_prop, font_size=11)

plt.title("關鍵詞關聯圖", fontsize=14, fontproperties=font_prop)
plt.axis('off')
plt.tight_layout()
plt.show()