A new link prediction algorithm (ZHA), based on comprehensive influence of predicting nodes and neighbor nodes to improve the accuracy and applicability of link prediction for complex networks, was proposed. Taking the comprehensive influence of predicting nodes and neighbor nodes into account, the new algorithm was constructed on the basis of the information of nodes in complex networks. ZHA was applied to seven real complex networks, and the random experiment was performed 10 times and 100 times, respectively, to identify its applicability and precision. Comparing the precision of ZHA with classical similarity link prediction algorithms, results showed that the new algorithm ZHA had higher precision. On the foundation of the experiments, the relationship between the accuracy of link prediction and experiment times was analyzed, and the principle of how to select experiment times was given.

The judgment service rate is an important index to reflect the fairness of the judgment of legal cases in a certain area, which is of great significance to verify the accuracy of a court judgment. In this paper, a grey neural network model combining grey system theory and BP neural network algorithm is proposed to predict the index. Analyze the judgment service rate of the court judgment system, and build a prediction system based on the completion rate, completion rate, plaintiff satisfaction, defendant satisfaction, litigation time, property preservation cycle, document delivery time, implementation information disclosure rate, and other key indicators. Through example analysis, it is proved that the combined model of the grey prediction model and BP neural network has a small error and good simulation effect on the prediction of court decision-making service rate, which can better promote the development of court and society.

Link prediction provides insight into the evolutionary mechanisms of complex networks by predicting missing edges. Existing research has proposed many similarity algorithms based on local information, and some link prediction algorithms typically perform better in different networks. It is generally believed that a megamerger is beneficial. In the perspective of link prediction, merging the good-performing algorithms brings higher prediction accuracy. And the more times the experiment is executed, the higher the accuracy of link prediction. Therefore, this research proposes a new link prediction algorithm based on the theory of megamerger in management and the concept of partnership, and uses ten actual complex networks for experiments to test the above two hypotheses. The experimental results show that megamerger is not applicable to the link prediction algorithm. In addition, there is no positive correlation between the increasing the quantity of experiments and improving the accuracy of the experiments, so the above two hypotheses are rejected. Hence, this research presumes that megamerger of the comprehensive information of the network, such as the resource flow between nodes, the degree of common neighbor nodes, and partnership of nodes, does not improve the accuracy of link prediction. For a refined network with a small number of nodes and a short average path length, it is recommended that the quantity of experiments be set to only ten can achieve the required accuracy of link prediction.INDEX TERMS Link prediction, megamerger, complex network.

When dealing with cases, judges must consult a large number of relevant materials and carefully consider before they can write the final judgment. So, we want to use intelligent systems to assist the judicial system in handling cases. The essence of the system is automatic text classification. The system can predict the judgment result according to the previous prediction and can also provide support for judicial judgment and individual litigation. Because the evaluation of intelligent judicial decision-making system has the characteristics of complexity and fuzziness, we establish a comprehensive evaluation model of intelligent judicial decision-making system with subjective and objective combination by introducing the TOPSIS model. In the experiment, firstly, we use nine multiattribute comprehensive evaluation index systems such as acquisition cost and use cost to grade the indexes. Secondly, AHP and entropy weight methods are used to calculate the subjective weight and objective weight of the index, respectively; the combined weight of the index is determined according to the expert forced scoring method, the attribute measurement function of a single index is constructed according to the classification of the index, the comprehensive attribute measurement is calculated, and the comprehensive evaluation grade is judged according to the attribute identification standard. Finally, taking the intelligent judicial decision-making system as the research object, combined with the system report and expert score, this paper makes a multiattribute comprehensive evaluation and analysis of the intelligent judicial decision-making system and analyzes the results. The final experimental results show that the evaluation results of the model are reasonable and consistent with the actual situation, which verifies the adaptability of the combined weighted attribute recognition model in the multiattribute comprehensive evaluation of intelligent judicial judgment system. This result provides ideas and theoretical follow-up work for the intelligent judgment of judicial cases and has certain significance for the development of the field of judicial judgment.

The distribution and scale of charging piles needs to consider the power allocation and environmental adaptability of charging piles. Through the multi-objective optimization modeling, the heuristic algorithm is used to analyze the distribution strategy of charging piles in the region, and the distribution of charging piles is determined to meet the minimum consumption of charging path, and then the construction scale is determined according to the calculation of environmental fitness. The rationalization of charging pile distribution and construction scale can achieve the effective allocation of distribution and transmission.