请求处理报错，谢谢

Amgalang

import jieba

import jieba.analyse

from gensim.test.utils import common_texts, get_tmpfile

from gensim.models import Word2Vec

#文件位置需要改为自己的存放路径

#将文本分词

with open(r'C:\Users\amgalang\Desktop\in_the_name_of_people.txt',encoding='utf-8') as f:

    document = f.read()

    document_cut = jieba.cut(document)

    result = ' '.join(document_cut)

    with open('./in_the_name_of_people_segment.txt', 'w',encoding="utf-8") as f2:

        f2.write(result)



#加载语料

sentences = word2Vec.LineSentence('./in_the_name_of_people_segment.txt')

#训练语料

path = get_tmpfile("word2vec.model") #创建临时文件

model = word2Vec.Word2Vec(sentences, hs=1,min_count=1,window=10,size=100)

# model.save("word2vec.model")

# model = Word2Vec.load("word2vec.model")

#输入与“贪污”相近的100个词

for key in model.wv.similar_by_word('贪污', topn =100):

    print(key)








代码跑完结果：

Building prefix dict from the default dictionary ...
Loading model from cache C:\Users\amgalang\AppData\Local\Temp\jieba.cache
Loading model cost 0.872 seconds.
Prefix dict has been built succesfully.
Traceback (most recent call last):
  File "C:/Users/amgalang/Desktop/3.py", line 30, in <module>
    sentences = word2Vec.LineSentence('./in_the_name_of_people_segment.txt')
NameError: name 'word2Vec' is not defined


希望处理报错，望大神们给予修改。

facevoid

这个word2Vec要大写成Word2Vec，要跟你加载的模块名称一致

zltzlt

word2Vec 要改成 Word2Vec

1. import jieba
   
2. 
   
3. import jieba.analyse
   
4. 
   
5. from gensim.test.utils import common_texts, get_tmpfile
   
6. 
   
7. from gensim.models import Word2Vec
   
8. 
   
9. #文件位置需要改为自己的存放路径
   
10. 
    
11. #将文本分词
    
12. 
    
13. with open(r'C:\Users\amgalang\Desktop\in_the_name_of_people.txt',encoding='utf-8') as f:
    
14. 
    
15.     document = f.read()
    
16. 
    
17.     document_cut = jieba.cut(document)
    
18. 
    
19.     result = ' '.join(document_cut)
    
20. 
    
21.     with open('./in_the_name_of_people_segment.txt', 'w',encoding="utf-8") as f2:
    
22. 
    
23.         f2.write(result)
    
24. 
    
25. 
    
26. 
    
27. #加载语料
    
28. 
    
29. sentences = word2Vec.LineSentence('./in_the_name_of_people_segment.txt')
    
30. 
    
31. #训练语料
    
32. 
    
33. path = get_tmpfile("word2vec.model") #创建临时文件
    
34. 
    
35. model = Word2Vec.Word2Vec(sentences, hs=1,min_count=1,window=10,size=100)
    
36. 
    
37. # model.save("word2vec.model")
    
38. 
    
39. # model = Word2Vec.load("word2vec.model")
    
40. 
    
41. #输入与“贪污”相近的100个词
    
42. 
    
43. for key in model.wv.similar_by_word('贪污', topn =100):
    
44. 
    
45.     print(key)

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