This study proposes a semantic integrity analysis method based on recurrent neural network. By judging whether the sentence is semantically complete, the long text is divided into multiple semantic complete sentences. First, dividing the sentences into words, mapped to the corresponding word vector and labeled. Then the word vector and the annotation information are processed by the loop window and the undersampling method, and used as the input of the recurrent neural network. Finally we get the model by training. The result of experiment indicates that this method can achieve an accuracy of 91.61%. This method is the basis of automatic assessment of the subjective questions, and also helps the research of semantic analysis, question and answer system and machine translation.