Aiming at the problems faced by college student management in the context of educational big data, this study proposes an academic early warning algorithm for college students. It mines potential education data with the results of digital campus construction in colleges and universities. Eight characteristic data with higher correlation coefficients selected by the Kendall correlation analysis are taken as the input for the BP neural network, and the relevant results are applied to improving the GA-BP algorithm. Thus, the academic situation is predicted by taking into account various factors. The tests demonstrate that the prediction accuracy of the proposed algorithm can reach more than 90%.