Asset allocation decision method for universities based on decision tree

Scientific and effective allocation of university assets is of great significance to improve the ability of university education guarantee. Decision tree is a decision analysis method based on the known probability of occurrence of various situations, which can calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility by constructing a decision tree. It is a graphic method of intuitively using probability analysis. In machine learning, decision tree is a prediction model, which represents a mapping relationship between object attributes and object values. In this paper, by quantifying the data of state-owned assets in colleges and universities to form attribute sample categories, we use Cart decision tree algorithm which build a binary tree to learn data samples, construct prediction criteria, give correct classification of asset allocation, and provide a more reasonable solution for asset allocation.