The research on data resource value evaluation algorithms is of great significance for the construction of the current enterprise data resource accounting method system. The article proposes an evaluation algorithm for enterprise data resources, which is based on fuzzy theory and combines Catastrophe theory to avoid the dependence of traditional state evaluation algorithms on subjective weights, as well as the overly complex characteristics of Monte Carlo simulation and artificial neural network models. The algorithm can better reflect value changes from the perspective of state transitions. It comprehensively considers the factors that affect the value changes of data resources and can provide reasonable evaluations even in the absence of parameters. It is also simpler and easier to implement. Using actual data as an example, the effectiveness and feasibility of the algorithm were verified through the analysis of evaluation results and detection data.