During the clustering process of high?dimensional data with feature missing， there are problems of the curse of dimensionality caused by data high dimension and the invalidity of effective distance calculation between samples caused by data feature missing. To resolve above issues， a K?Nearest Neighbor （KNN） imputation subspace clustering algorithm for high?dimensional data with feature missing was proposed， namely KISC. Firstly， the nearest neighbor relationship in the subspace of the high?dimensional data with feature missing was used to perform KNN imputation on the feature missing data in the original space. Then， multiple iterations of matrix decomposition and KNN imputation were used to obtain the final reliable subspace structure of the data， and the clustering analysis was performed in that obtained subspace structure. The clustering results in the original space of six image datasets show that the KISC algorithm has better performance than the comparison algorithm which clusters directly after interpolation， indicating that the subspace structure can identify the potential clustering structure of the data more easily and effectively； the clustering results in the subspace of six high?dimensional datasets shows that the KISC algorithm outperforms the comparison algorithm in all datasets， and has the optimal clustering Accuracy and Normalized Mutual Information （NMI） on most of the datasets. The KISC algorithm can deal with high?dimensional data with feature missing more effectively and improve the clustering performance of these data.

Learning the latent vector representations of nodes in the graph is an important and ubiquitous task， which aims to capture various attributes of the nodes in the graph. A lot of work demonstrates that static graph representation learning can learn part of the node information； however， real-world graphs evolve over time. In order to solve the problem that most dynamic network algorithms cannot effectively retain node neighborhood structure and temporal information， a dynamic network representation learning method based on Deep Neural Network （DNN） and Gated Recurrent Unit （GRU）， namely DynAEGRU， was proposed. With Auto-Encoder （AE） as the framework of the DynAEGRU， the neighborhood information was aggregated by encoder with a DNN to obtain low-dimensional feature vectors， then the node temporal information was extracted by a GRU network，finally， the adjacency matrix was reconstructed by the decoder and compared with the real graph to construct the loss. Experimental results on three real-word datasets show that DynAEGRU method has better performance gain compared to several static and dynamic graph representation learning algorithms.

In order to solve the problem of low efficiency of flowchart drawing and better guarantee the consistency of software model, document and code, an algorithm for automatic generation of flowchart was proposed. Firstly, by analyzing the C/C++ source code in reverse, the Token list of the code was extracted, and the Scope tree was created to realize the flowchart generation. At the same time, a method for regulating the annotation of code functions was proposed, improving the comprehensibility of the flowchart. Finally, the readable flowchart was generated after the automatic layout of flowchart by applying the Sugiyama layout algorithm and completing and improving the coordinate designation step. In the actual application process, with the use of the proposed algorithm, the efficiency of writing software design documents is effectively improved and the consistency of the software model, document and code is guaranteed.