To reduce the probability of violent crimes, the deep learning (DL) technology and linear spatial autoregressive models (ARMs) are utilised to estimate the model parameters through different penalty functions. In addition, under a determinate space, the influences of environmental factors on violent crimes are discussed. By taking campus violence cases as examples, the major influencing factors of violent crimes are found through data analysis. The results show that campus violence cases are usually caused by the complex surrounding environments and persons. Also, campus security measures only cover a small range, and the security management is difficult. In the meantime, due to the younger ages and lack of self-protection awareness, students may easily become the targets of criminals. Therefore, the results have a positive significance for authorities to analyse the crime rates in a determinate area and take preventive measures against violent crimes.

The dynamic nonlinear system with saturation constraints and unknown information is studied to design a better control system. The controller of the general n-order affine nonlinear system is constructed utilizing the backstepping method and the improved radial basis function (RBF) that can approximate the unknown parameters. Then, the appropriate Lyapunov function is selected. The mature stability judgment theorem can prove that the designed system is semi-globally uniform and ultimately bounded. Finally, the controller is added to the system for the experiment on MATLAB. The experimental results show that the controller is reliable and the system output can approach the ideal value through the limited range of control signal. During the simulation, the reference value can be quickly tracked by the system output, and the tracking error is small only within 0.04. The proposed method can provide a reference for the design of control systems in practical engineering.