Working memory complex network analysis methods mostly use channels as nodes to analyze from the perspective of space, while rarely analyze channel networks from the perspective of time. Focused on the high time resolution characteristics of ElectroEncephaloGraph (EEG) and the difficulty of time series segmentation, a method of constructing and analyzing network from the time perspective was proposed. Firstly, the microstate was used to divide EEG signal of each channel into different sub-segments as nodes of the network. Secondly, the effective features in the sub-segments were extracted and selected as the sub-segment effective features, and the correlation between sub-segment feature vectors was calculated to construct channel time sequence complex network. Finally, the attributes and similarity analysis of the constructed network were analyzed and verified on the schizophrenic EEG data. The experimental results show that the analysis of schizophrenia data by the proposed method can make full use of the time characteristics of EEG signals to understand the characteristics of time series channel network constructed in working memory of patients with schizophrenia from a time perspective, and explain the significant differences between patients and normals.

Aiming at the problem of data sparsity, poor portability and lack of recognition of multiple words (more than three words) in micro-blog new word discovery algorithm, a new word discovery algorithm based on improved Mutual Information (MI) and Branch Entropy (BE), named MBN-Gram, was proposed. Firstly, the N-Gram was used to extract the candidate terms of new words, and the rules of using frequency and stop words were used to filter the candidates. Then the improved MI and BE were used to expand and filter the candidates again. Finally, the corresponding dictionary was used to screen, so as to get new words. Theoretical and experimental analysis show that the accuracy rate, recall rate and F value of MBN-Gram algorithm were improved. Experimental results shows that the MBN-Gram algorithm is effective and feasible.

There exists the problem of security insufficience among the data privacy protecting technology which is the privacy leakage caused by homogeneity and background knowledge attack when computing equivalence classes in the anonymity process. To solve the problem, an anonymized data privacy protection method based on differential privacy was put forward, and its model was constructed. ε-MDAV (Maximum Distance to Average Vector) algorithm was presented, in which micro-aggregation MDAV algorithm was used to partition similar equivalence classes, and SuLQ frame framework was introduced into the anonymous attribute process. Laplace mechanism was used to reasonably control the privacy protection budget. The comparison of availability and security under different privacy protect budgets verifies that the proposed method effectively improve data security while guaranteeing high data availability.