The existing image captioning methods only focus on the grid spatial location features without enough grid feature interaction and full use of image global features. To generate higher-quality image captions， a global image captioning method based on Graph ATtention network （GAT） was proposed. Firstly， a multi-layer Convolutional Neural Network （CNN） was utilized for visual encoding， extracting the grid features and entire image features of the given image and building a grid feature interaction graph. Then， by using GAT， the feature extraction problem was transformed into a node classification problem， including a global node and many local nodes， and the global and local features were able to be fully utilized after updating the optimization. Finally， through the Transformer-based decoding module， the improved visual features were adopted to realize image captioning. Experimental results on the Microsoft COCO dataset demonstrated that the proposed method effectively captured the global and local features of the image， achieving 133.1% in CIDEr （Consensus-based Image Description Evaluation） metric. It can be seen that the proposed image captioning method is effective in improving the accuracy of image captioning， thus allowing processing tasks such as classification， retrieval， and analysis of images by words.

In the analysis process of high-dimensional data such as image data, genetic data and text data, when samples have redundant features, the complexity of the problem is greatly increased, so it is important to reduce redundant features before data analysis. The feature selection based on Mutual Information (MI) can reduce the data dimension and improve the accuracy of the analysis results, but the existing feature selection methods cannot reasonably eliminate the redundant features because of the single standard. To solve the problem, a feature selection method based on Maximum Conditional and Joint Mutual Information (MCJMI) was proposed. Joint mutual information and conditional mutual information were both considered when selecting features with MCJMI, improving the feature selection constraint. Exerimental results show that the detection accuracy is improved by 6% compared with Information Gain (IG) and minimum Redundancy Maximum Relevance (mRMR) feature selection; 2% compared with Joint Mutual Information (JMI) and Joint Mutual Information Maximisation (JMIM); and 1% compared with LW index with Sequence Forward Search algorithm (SFS-LW). And the stability of MCJMI reaches 0.92, which is better than JMI, JMIM and SFS-LW. In summary the proposed method can effectively improve the accuracy and stability of feature selection.

Aiming at the resource scheduling problem across data centers, a Priority Combinatorial Double Auction (PCDA) resource scheduling scheme was proposed. Firstly, cloud resource auction was divided into three parts:cloud user agent bidding, cloud resource provider bid, auction agent organization auction. Secondly, on the basis of defining user priority and task urgency, the violation of Service Level Agreement (SLA) of each job during auction was estimated and the revenue of cloud provider was calculated. At the same time, a number of transactions were allowed in each round of bidders. Finally, reasonable allocation of cloud resource scheduling according to user level could be achieved. The simulation results show that the algorithm guarantees the success rate of auction. Compared with traditional auction, PCDA reduces energy consumption by 35.00% and the profit of auction cloud provider is about 38.84%.