When the Unmanned Aerial Vehicle （UAV） cluster attacks ground targets， it will be divided into two formations： a strike UAV cluster that attacks the targets and a auxiliary UAV cluster that pins down the enemy. When auxiliary UAVs choose the action strategy of aggressive attack or saving strength， the mission scenario is similar to a public goods game where the benefits to the cooperator are less than those to the betrayer. Based on this， a decision method for cooperative combat of UAV clusters based on deep reinforcement learning was proposed. First， by building a public goods game based UAV cluster combat model， the interest conflict problem between individual and group in cooperation of intelligent UAV clusters was simulated. Then， Muti-Agent Deep Deterministic Policy Gradient （MADDPG） algorithm was used to solve the most reasonable combat decision of the auxiliary UAV cluster to achieve cluster victory with minimum loss cost. Training and experiments were performed under conditions of different numbers of UAV. The results show that compared to the training effects of two algorithms — IDQN （Independent Deep Q-Network） and ID3QN （Imitative Dueling Double Deep Q-Network）， the proposed algorithm has the best convergence， its winning rate can reach 100% with four auxiliary UAVs， and it also significantly outperforms the comparison algorithms with other UAV numbers.

Aiming at the problem that traditional link prediction has computational results not stable in networks with different structures， a link prediction algorithm based on information entropy improved Principal Component Analysis （PCA） model was proposed. Firstly， seven similarity indexes were determined by Random Forest （RF） as the optimal feature set. Then， seven similarity indexes were combined to propose a feature information fusion model based on information entropy improved PCA. After weighting the feature information， the model was combined with the single mechanism algorithms to verify the correctness and verification effect of the model on six real-world datasets. Finally， the feasibility and effectiveness of the link prediction algorithm based on the proposed model were verified by comparing Area Under the Curve （AUC） values with the hybrid link prediction algorithms. Experimental results show that the proposed link prediction algorithms improve the AUC value by 2.5 to 12.46 percentage points and 0.47 to 9.01 percentage points， respectively， compared with Ordered Weighted Averaging aggregation operator （OWA） and Ensemble-Model-based Link Prediction algorithm （EMLP）. It can be seen that applying the proposed algorithm to networks with different structural features can obtain more stable and accurate link prediction results.

For the poor precision of region-based image retrieval, Multi-Instance Learning (MIL) prototype selection algorithm and feedback mechanism with reference to textual information were proposed. In the process of instance prototype selection, textual information was used to extend the positive examples, and negative example distribution was used to select initial instances and the iterative optimization process of instance updating and classifier training were used to obtain the true instance prototypes. In the process of relevance feedback, active learning with the combined learning methods was adopted. The switch of active learning strategy was controlled by the information value in the feedback process. The experimental results show that this algorithm is feasible, and the performance is superior to other MIL algorithms.