中文本文分词、去停用词、关键词提取、向量化、文本相似度计算代码

以下是Python代码实现，需要使用jieba、pandas、sklearn等库：

1. 分词

import jieba

def cut_words(text):
    words = jieba.cut(text)
    return ' '.join(words)

2. 去停用词

import pandas as pd

def remove_stopwords(words):
    stopwords = pd.read_csv('stopwords.txt', index_col=False, quoting=3, sep='\t', names=['stopword'], encoding='utf-8')
    words = words.split(' ')
    words = [word for word in words if word not in stopwords]
    return ' '.join(words)

3. 关键词提取

from sklearn.feature_extraction.text import TfidfVectorizer

def get_keywords(text):
    vectorizer = TfidfVectorizer(max_features=200)
    X = vectorizer.fit_transform([text])
    feature_names = vectorizer.get_feature_names()
    keywords = [feature_names[index] for index in X.toarray()[0].argsort()[::-1][:10]]
    return keywords

4. 向量化

def get_vectors(text1, text2):
    vectorizer = TfidfVectorizer()
    X = vectorizer.fit_transform([text1, text2])
    return X.toarray()

5. 文本相似度计算

from sklearn.metrics.pairwise import cosine_similarity

def get_similarity(text1, text2):
    vectors = get_vectors(text1, text2)
    similarity = cosine_similarity(vectors[0].reshape(1, -1), vectors[1].reshape(1, -1))[0][0]
    return similarity

以上代码仅供参考，实际应用时需要根据具体需求进行修改。