Aiming at the problems that the dimensions of vectors generated by Traditional Vector Space Model （TVSM） are high， and the vector dot product operation to calculate the correlation between the documents and the queried keywords is time-consuming， a Dictionary Partition Vector Space Model （DPVSM） for ciphertext ranked search in cloud environment was proposed. Firstly， the specific definition of DPVSM was given， and it was proved that the relevance score between the queried keywords and the documents in DPVSM was exactly the same as that in TVSM. Then， by adopting the equal-length dictionary partition method， an encrypted vector generation algorithm and a relevance score calculation algorithm between documents and queried keywords were proposed. Experimental results show that the space occupation of document vectors of DPVSM is much lower than that of TVSM， and the more the number of documents， the greater the occupation reduction. In addition， the space occupation of query vectors and the time consumption of relevance score calculation are also much lower than those of TVSM. Obviously， DPVSM is superior to TVSM in both the space efficiency of generated vectors and the efficiency cost of relevance score calculation.

All market activities of stock market participants combine to affect stock market changes， making stock market volatility fraught with complexity and making accurate prediction of stock prices a challenge. Among these activities that affect stock market changes， financial disclosure is an attractive and potentially financially rewarding means of predicting stock indexe changes. In order to deal with the complex changes in the stock market， a method of stock index prediction was proposed that incorporates data from financial statements disclosed by corporates. Firstly， the stock index historical data and corporate financial statement data were preprocessed， and the main task is dimension reduction of the high-dimensional matrix generated from corporate financial statement data， and then the dual-channel Long Short-Term Memory （LSTM） network was used to forecast and research the normalized data. Experimental results on SSE 50 and CSI 300 Index datasets show that the prediction effect of the proposed method is better than that using only historical data of stock indexes.

Multiple feature fusion based tracking is one of the most active research topic in tracking field, but the tracking accuracy needs improving in complex environment and most of them use single fusion rule. In this paper, a new adaptive fusion strategy was proposed for multi-feature fusion. First, the local background information was introduced to strengthen the description of the target, and then the feature weight was calculated by a variety of criteria in the fusion process. In addition, the framework of mean shift was considered to realize target tracking. An extensive number of comparative experimental results show that the proposed algorithm is more stable and robust than the single fusion rule.

As surgeons do not have effective prediction on the the implant surgery for vocal cord paralysis, resulting in high rate of failure, the finite element method was used for preoperative simulation. Through Computed Tomography (CT) data of larynx, the 3D geometric model of vocal cords and glottis trachea was extracted by Mimics, and then imported into ANSYS-Fluent to simulate the vocal vibration mode and airflow dynamic coupling characteristics before and after implanted surgery. The experimental data and clinical statistics data were compared to prove the feasibility of the finite element analysis techniques for implant surgery simulation of vocal cord paralysis. The experimental result can provide support for the design of surgery program.