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Soft sensors are widely used in industry to predict key process variables that are closely related to product quality, and these variables are difficult to measure online. To build a high-precision soft sensor, it is important to choose the appropriate auxiliary variables. Aiming at this problem, this paper obtains a mixed integer nonlinear programming problem by coupling the BIC criterion of the training set and the MSE criterion of the verification set, and divides the mixed integer nonlinear programming problem into two layers, the inner and outer layers, and the outer layer uses the Genetic Algorithm (GA). The integer variable is optimized, and the inner layer degenerates into an easier to solve nonlinear programming problem (NLP) after the integer variable is fixed. Based on this analysis, a variable selection method based on hybrid criteria is proposed. Then the subset of secondary variables obtained is substituted into BP neural network for soft sensor modeling. Finally, the proposed method is validated by four actual cases. The results show that the soft-measurement model established by the proposed method has better prediction performance.