Industrial investment is a common development method in economic development, essentially a process of transferring funds according to future industrial development trends. The correctness of industrial investment directly affects the process of economic development. Various provinces in China have also adopted the method of industrial investment to achieve economic transformation, but research has found that industrial investment has not caused a qualitative change in China’s economy. Faced with such problems, China must further expand the scale and intensity of industrial investment, and focus on high-tech industries with high, precision, and cutting-edge capabilities to promote economic development. Therefore, this article explored the effectiveness of industrial investment efficiency based on the Support Vector Machine (SVM) model, with the aim of constructing a model for predicting industrial investment efficiency through the SVM model algorithm. This article first collected data related to industrial investment efficiency, then processed the data and selects appropriate feature variables to input into the SVM model. Finally, through experimental simulation analysis of classification accuracy, the validity prediction accuracy of the test samples can reach 100%, indicating that the SVM prediction model can provide reference value for the initial stage of industrial investment budgeting.