下面是gensim的例子，但每当我执行它时， 它显示出不同的结果，所以我无法相信gensim效果很好。
from gensim import corpora, models, similarities
from collections import defaultdict
documents = ["Human machine interface for lab abc computer applications", # 0
"A survey of user opinion of computer system response time", # 1
"The EPS user interface management system", # 2
"System and human system engineering testing of EPS", # 3
"Relation of user perceived response time to error measurement", # 4
"The generation of random binary unordered trees", # 5
"The intersection graph of paths in trees", # 6
"Graph minors IV Widths of trees and well quasi ordering", # 7
"Graph minors A survey"] # 8
stoplist = set('for a of the and to in'.split())
texts = [[word for word in document.lower().split() if word not in stoplist]
for document in documents]
frequency = defaultdict(int)
for text in texts:
for token in text:
frequency[token] += 1
texts = [[token for token in text if frequency[token] > 1]
for text in texts]
dictionary = corpora.Dictionary(texts)
corpus = [dictionary.doc2bow(text) for text in texts]
lda = models.LdaModel(corpus, id2word=dictionary, num_topics=2)
index = similarities.MatrixSimilarity(lda[corpus])
doc = "Human computer interaction"
vec_bow = dictionary.doc2bow(doc.lower().split())
vec_lda = lda[vec_bow]
sims = index[vec_lda]
sims = sorted(enumerate(sims), key=lambda item: -item[1])
print(sims)
print(lda.get_document_topics(vec_bow))
结果
[（ 0 ，0.9986434），（4,0.99792993），（2,0.99722278），（3,0.99651831），（1,0.9958639），（5,0.53059661），（6， 0.4146674），（8,0.38019019），（7,0.36143348）] [（0,0.18366596），（1,0.81633401）]
[（ 1 ，0.999605），（4,0.9981864），（0,0.998689），（5,0.62957084），（6,0.48837978），（8,0.45152202），（3， 0.4541581），（7,0.41751832），（2,0.40637407）] [（0,0.80285221），（1,0.19714773）]
[（ 7 ，0.99957085），（8,0.99660784），（0,0.99202132），（5,0.78449017），（6,0.77530348），（2,0.56972337），（3， 0.47117239），（4,0.47092015），（1,0.4172135）] [（0,0.25292286），（1,0.7707717）]
结果7看起来与“人机交互”看起来并不相似。 感谢。