  Natural language processing (NLP) task has achieved excellent performance in
many fields, including semantic understanding, automatic summarization, image
recognition and so on. However, most of the neural network models for NLP
extract the text in a fine-grained way, which is not conducive to grasp the
meaning of the text from a global perspective. To alleviate the problem, the
combination of the traditional statistical method and deep learning model as
well as a novel model based on multi model nonlinear fusion are proposed in
this paper. The model uses the Jaccard coefficient based on part of speech,
Term Frequency-Inverse Document Frequency (TF-IDF) and word2vec-CNN algorithm
to measure the similarity of sentences respectively. According to the
calculation accuracy of each model, the normalized weight coefficient is
obtained and the calculation results are compared. The weighted vector is input
into the fully connected neural network to give the final classification
results. As a result, the statistical sentence similarity evaluation algorithm
reduces the granularity of feature extraction, so it can grasp the sentence
features globally. Experimental results show that the matching of sentence
similarity calculation method based on multi model nonlinear fusion is 84%, and
the F1 value of the model is 75%.
