In order to measure the similarity of multi-relational nodes and mine the community structure with multi-relational nodes， a community mining algorithm based on multi-relationship of nodes， called LSL-GN， was proposed. Firstly， based on node similarity and node reachability， LHN-ISL， a similarity measurement index for multi-relational nodes， was described to reconstruct the low-density model of the target network， and the community division was completed by combining with GN （Girvan-Newman） algorithm. The LSL-GN algorithm was compared with several classical community mining algorithms on Modularity （Q value）， Normalized Mutual Information （NMI） and Adjusted Rand Index （ARI）. The results show that LSL-GN algorithm achieves the best results in terms of three indexes， indicating that the community division quality of LSL-GN is better. The “User-Application” mobile roaming network model was divided by LSL-GN algorithm into community structures based on basic applications such as Ctrip， Amap and Didi Travel. These results of community division can provide strategic reference information for designing personalized package services.

Ensemble resampling technology can solve the problem of imbalanced samples in financial early warning research to some extent. Different ensemble models and different ensemble resampling technologies have different suitabilities. It is found in the study that Up-Down ensemble sampling and Tomek-Smote ensemble sampling were respectively suitable for Bagging-Vote ensemble model and Stacking fusion model. Based on the above， a Stacking-Bagging-Vote （SBV） multi-source information fusion model was built. Firstly， the Bagging-Vote model based on Up-Down ensemble sampling and the Stacking model based on Tomek-Smote sampling were fused. Then， the stock trading data were added and processed by Kalman filtering， so that the interactive fusion optimization of data level and model level was realized， and the SBV multi-source information fusion model was finally obtained. This fusion model not only has a great improvement in the prediction performance by taking into account prediction accuracy and prediction precision simultaneously， but also can select the corresponding SBV multi-source information fusion model to perform the financial early warning to meet the actual needs of different stakeholders by adjusting the parameters of the model.

In the domain of structural pattern recognition, the existing graph embedding methods lack versatility and have high computation complexity. A new graph embedding method integrated with multiscale features based on space syntax theory was proposed to solve this problem. This paper extracted the global, local and detail features to construct feature vector depicting the graph feature by multiscale histogram. The global features included vertex number, edge number, and intelligible degree. The local features referred to node topological feature, edge domain features dissimilarity and edge topological features dissimilarity. The detail features comprised numerical and symbolic attributes on vertex and edge. In this way, the structural pattern recognition was converted into statistical pattern recognition, thus Support Vector Machine (SVM) could be applied to achieve graph classification. The experimental results show that the proposed graph embedding method can achieve higher classifying accuracy in different graph datasets. Compared with other graph embedding methods, the proposed method can adequately render the graphs topology, merge the non-topological features in terms of the graphs domain property, and it has a favorable universality and low computation complexity.

To extend the application range of Trusted Cryptography Module (TCM) and promote the safety and credibility on terminal machine and cloud platform, this paper analyzed the status quo and tendency of TCM firmware, proposed a TCM firmware driver framework based on Unified Extensible Firmware Interface (UEFI), and designed low-level the driver interface and core protocol based on this framework. This TCM driver adopted module design and layered implementation, made the TCM protocol packaged and registered to UEFI firmware system, and completed the low-level data sending and receiving as well as protocol encapsulation. The test results of TCM firmware driver indicate the high accuracy and effectiveness for this design through the conformance test, functional test as well as pressure test. Besides, the industrial situation also illustrates the feasibility of this driver.

To improve the recognition rate of Principal Component Analysis (PCA) algorithm in face recognition, a new algorithm combining the image texture spectrum feature with PCA was proposed. Firstly, the texture unit operator was used to extract the texture spectrum feature of the face image. Secondly, PCA approach was used to reduce the dimensions of the texture spectrum feature. Finally, K-Nearest Neighbor (KNN) classification was chosen to recognize the face. ORL and Yale face database were used to test the proposed algorithm, and the recognition accuracies were 96.5% and 95% respectively, which were higher than those of PCA and Modular Two-Dimensional PCA (M2DPCA). The experimental results demonstrate the efficiency and accuracy of the proposed algorithm.

To analyze both the operational mechanism of current distributed data mining and the characteristics of the P2P technology: non-centralized peer and asynchronism, by extending the iterative process of classical K-mean algorithm, a distributed data mining algorithm was designed in this paper to implement k-mean thinking in a P2P networks. This algorithm exchanges information only between directly connected nodes, and can cluster local data on each peer in a global view. Finally, simulation experiments show that the algorithm is effective and accurate.