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.

Today's world revolves around technology, which has a total impact not only on human life but also on manufacturing companies. Many companies have embraced artificial intelligence (AI) in the form of powerful computers, applications, or software that can screen job applicants, alert when a machine is about to break down, and read legal contracts. However, the rapid expansion of AI and its use in legal settings, such as contract performance, in a company is a major challenge on the judicial side. This article, thus, establishes an evolutionary game model of whether manufacturing suppliers are performing contracts or not when the court chooses to use artificial intelligence (AI) technology. Considering the complexity of choosing manufacturers' AI strategy, the method constructs a simulation analysis model of manufacturers' contract enforcement behaviour with the participation of several subjects. We can simulate the influence of the factors selected on the strategy chosen by both parties (manufacturers and court) by changing the different influence factors and studying the evolutionary law of different court guidance and regulation strategies on the production behaviour of green products. The results show that the choice of the court and manufacturers to use the AI technology strategy or not is based on the rate of error reduction, through the computational implementation of multi-subject modelling.

Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA‐II algorithm. This paper established a six‐tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA‐II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained.

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.