In recent years, with the increasing demand for self-study space in college libraries, traditional space management methods often rely on experience and manual intervention, which lack scientificity and precision. In this paper, a method for optimizing the effectiveness of library space management based on the Improved Gray Wolf Optimization (IGWO) algorithm and Support Vector Regression (SVR) model is proposed. First, the key parameters of SVR are optimized using the gray wolf optimization algorithm to improve the prediction accuracy of the regression model. Then, it is experimentally verified that the improved Gray Wolf algorithm has superior accuracy and convergence speed compared with the traditional GWO algorithm. In the experiment, the root mean square error (RMSE) of the IGWO-SVR model is 0.0008, the mean absolute error (MAE) is 0.0057, and the coefficient of determination (R²) reaches 99.95%. In addition, the maximum absolute error (MAE) of the IGWO-SVR model in predicting the spatial effectiveness of libraries is 0.0057, which is significantly lower than the errors of the BPNN model and the traditional SVR model. The experimental results show that the improved SVR model can not only accurately predict library space management effectiveness, but also provide theoretical support and practical basis for optimizing resource allocation and enhancing management services.