Infrared small targets occupy few pixels and lack features such as color， texture and shape， so it is difficult to track them effectively. To solve this problem， an infrared small target tracking method based on state information was proposed. Firstly， the target， background and distractors in the local area of the small target to be detected were encoded to obtain dense local state information between consecutive frames. Secondly， feature information of the current and the previous frames were input into the classifier to obtain the classification score. Thirdly， the state information and the classification score were fused to obtain the final degree of confidence and determine the center position of the small target to be detected. Finally， the state information was updated and propagated between the consecutive frames. After that， the propagated state information was used to track the infrared small target in the entire sequences. The proposed method was validated on an open dataset DIRST （Dataset for Infrared detection and tRacking of dim-Small aircrafT）. Experimental results show that for infrared small target tracking， the recall of the proposed method reaches 96.2%， and the precision of the method reaches 97.3%， which are 3.7% and 3.7% higher than those of the current best tracking method KeepTrack. It proves that the proposed method can effectively complete the tracking of small infrared targets under complex background and interference.

In order to improve the efficiency of doctor-patient dialogue by enabling doctors to quickly propose reasonable rhetorical questions in intelligent consultation， a method of rhetorical question generation based on deep neural network was proposed. Firstly， a large number of doctor-patient dialogue texts were obtained and labeled. Then， two classification models， Text Recurrent Neural Network （TextRNN） and Text Convolutional Neural Network （TextCNN）， were used to classify doctor’s statements respectively. Then， Text Recurrent Neural Network-Bidirectional Long Short-Term Memory （TextRNN-B） and Bidirectional Encoder Representations from Transformers （BERT） classification models were used to trigger questions. Six different Q&A selection methods were designed to simulate the situations in the field of medical consultation. Then， Open-Source Neural Machine Translation （OpenNMT） model was used to generate rhetorical questions. Finally， the generated rhetorical questions were evaluated comprehensively. Experimental results show that TextRNN is better than TextCNN in classification， and BERT model is better than TextRNN-B in question triggering； when OpenNMT model is used to realize rhetorical question generation in Window-top mode， the best results are obtained by using two evaluation indexes： Bilingual Evaluation Understudy （BLEU） and Perplexity （PPL）. The proposed method verifies the effectiveness of deep neural network technology in the generation of rhetorical questions， which can effectively solve the problem of doctor-patient question generation.