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浠ヤ笅鏄竴涓ず渚嬩唬鐮侊紝婕旂ず濡備綍鍦╯paCy涓繘琛屾枃鏈仛绫伙細

import spacy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

nlp = spacy.load("en_core_web_sm")

# 鍔犺浇鏂囨湰鏁版嵁
data = ["This is an example sentence.",
        "Another example sentence is here.",
        "I am writing a sample text for clustering.",
        "Text clustering is a useful technique."]

# 鏂囨湰棰勫鐞?/span>
processed_data = [nlp(text) for text in data]

# 鎻愬彇鏂囨湰鐗瑰緛鍚戦噺
tfidf_vectorizer = TfidfVectorizer()
tfidf_matrix = tfidf_vectorizer.fit_transform([text.text for text in processed_data])

# 浣跨敤K鍧囧€艰仛绫荤畻娉曡繘琛屾枃鏈仛绫?/span>
kmeans = KMeans(n_clusters=2)
clusters = kmeans.fit_predict(tfidf_matrix)

# 鍙鍖栬仛绫荤粨鏋?/span>
plt.scatter(tfidf_matrix.toarray()[:, 0], tfidf_matrix.toarray()[:, 1], c=clusters, cmap='viridis')
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.show()

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