With the rapid development of big data, big data research in the security protection industry has been increasingly regarded as a hot spot. This article mainly aims at solving the problem of predicting the tendency of juvenile delinquency based on the experimental data of juvenile blindly following psychological crime. To solve this problem, this paper proposes a rough ant colony classification algorithm, referred to as RoughAC, which first uses the concept of upper and lower approximate sets in rough sets to determine the degree of membership. In addition, in the ant colony algorithm, we use the membership value to update the pheromone. Experiments show that the algorithm can not only solve the premature convergence problem caused by stagnation near the local optimal solution but also solve the continuous domain and combinatorial optimization problems and achieve better classification results. Moreover, the algorithm has a good effect on predicting classification and can provide guidance for predicting the tendency of juvenile delinquency.

中文翻译：

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粗糙蚁群算法在青少年心理学中的应用

随着大数据的飞速发展，安全保护行业的大数据研究已日益成为热点。本文主要针对基于心理犯罪后的青少年犯罪实验数据，解决预测青少年犯罪倾向的问题。为了解决这个问题，本文提出了一种粗糙的蚁群分类算法，称为RoughAC，该算法首先使用粗糙集中的上下近似集的概念来确定隶属度。另外，在蚁群算法中，我们使用隶属度值更新信息素。实验表明，该算法不仅可以解决局部最优解附近停滞引起的过早收敛问题，而且可以解决连续域和组合优化问题，取得更好的分类效果。此外，该算法对分类预测有很好的效果，可以为预测青少年犯罪倾向提供指导。