Design of computer information management system based on machine learning algorithms

In order to improve the efficiency of office automation, regulate work frequency, and improve office efficiency, this paper presents a computer information management system design based on machine learning technology. Firstly, the basic design principles of computer information management systems are analyzed, and secondly, risk prediction is studied. The risk of computer information management systems is caused by the cross influence of different risk factor indicators, and has linear and nonlinear characteristics. Using a single prediction model cannot obtain accurate prediction results. Therefore, the risk prediction method for computer information management based on machine learning technology. The risk prediction method is established by using Analytic Hierarchy Method in machine learning algorithms, and the historical data is collected according to the index system. The weight of the initial prediction is determined by the combination of subjective and objective weight; In machine learning algorithms, risk prediction and benefit prediction are used as input and output methods for cloud machine learning. Through training and training, a risk prediction model is established to obtain higher prediction efficiency. The simulation results show that the prediction accuracy of this method is 95.5%, which can estimate the hazard existing in computer information management and improve the method.