Focusing on the problem that the Retrieval Matching Question and Answer (RMQA) model has weak adaptability to Chinese corpus and the neglection of semantic information of the sentence, a Chinese text semantic matching model based on Convolutional neural network Latent Semantic Model (CLSM) was proposed. Firstly, the word- N-gram layer and letter- N-gram layer of CLSM were removed to enhance the adaptability of the model to Chinese corpus. Secondly, with the focus on vector information of input Chinese words, an entity attention layer model was established based on the attention mechanism algorithm to strengthen the weight information of the core words in sentence. Finally, Convolutional Neural Network (CNN) was used to capture the input sentence context structure information effectively and the pool layer was used to reduce the dimension of semantic information. In the experiments based on a medical question and answer dataset, compared with the traditional semantic models, traditional translation models and deep neural network models, the proposed model has 4-10 percentage points improvement in Normalized Discount Cumulative Gain (NDCG).

To improve the performance of the co-training algorithm and expand the range of applications, a multi-view semi-supervised collaboration classification algorithm with the combination of consistent and inconsistent label rules was proposed, which aimed at providing a more effective method for the classification of the bridge structured health data. The proposed algorithm used combination of agreement and disagreement label rules for the unlabeled data by judging whether the two classifiers were consistent. Put the sample to the label set, if the label results were consistent. If the label results were inconsistent and the confidence was beyond the threshold, it put the label result of the high confidence to the label set, took full use of the unlabeled data to improve the performance of the classifier, and updated the classification model by the difference of the classifiers. The experimental results of the proposed algorithm on the bridge structured health datasets and standard UCI datasets verify the effectiveness and feasibility of the proposed model on the multi-view classification problems.