Environmental problem is an international problem that transcends national boundaries and develops into regional and global environmental pollution and ecological problems. Facing the increasing environmental pollution, the international community has successively formulated many relevant environmental pollution prevention laws, but the world situation is complicated after all, environmental problems still emerge endlessly, and the protection of environmental rights has become the consensus of the international community. Environmental right is an integral part of human rights, and protecting environmental right is the concrete expression and proper meaning of protecting human rights. Using international criminal law to protect environmental rights will play a positive role in global environmental protection. As with the development of computer technology, the research of machine learning has gradually transferred to the field of social science, especially the judicial field. While sentencing is a crucial part of environmental crime, this paper studies the sentencing of environmental rights cases from the perspective of international criminal law and uses Convolutional Neural Networks (CNN) to determine the sentencing of environmental rights cases. Through the experiment on the Integrated Database (IDB) dataset, the results show that the introduction of CNN improves the effect of the sentencing term prediction model and the fine prediction model significantly. The CNN-based model scored 91.6542 in the sentencing term prediction model and 90.8890 in the fine prediction model.

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In order to study the spatial structure characteristics and optimal layout of regional air logistics networks, a quantitative analysis system for the spatial structure of directed multivalued networks is constructed based on relational data using the social network analysis (SNA) method. Core-edge structure, cohesive subgroups, Burt’s structure hole, and topological analysis models were used. Characteristics of logistics spatial linkages of cities in the Yangtze River Economic Zone, the identification of logistics centre cities, and the construction of hub-and-spoke networks are explored, and the driving forces for the formation of logistics spatial linkage patterns are analysed using the Durbin model. The complexity of the designed model algorithm is investigated, and it is found that the comprehensive logistics capacity of the Yangtze River Economic Belt region is increasing year by year, and the strength of interregional linkages is also growing, showing a spatially divergent trend of “strong in the east and weak in the west”; the multicore network linkage pattern is obvious, and the structure of the spatial linkage network is gradually becoming more complex.