Soft sensing has been extensively used in industrial processes to predict key quality variables. To establish an accurate sensor model, it is important to select appropriate auxiliary variables. In this paper, Random Forest optimized Mallow's Cp variable selection scheme is proposed for accurate soft sensing. Firstly, the method employs the Random Forest algorithm to rank the importance of variables. Then, it gradually selects variable subsets and calculates the Cp value for each subset to determine the optimal subset. Once the optimal subset is confirmed, we utilize it to train a Support Vector Regression model and evaluate the model's performance. The method considers both individual and combinations of multiple auxiliary variables on the target variable, which can address the problem of excessive auxiliary variables and redundant data in industrial production processes and reduce computation complexity. Finally, the penicillin fermentation process verifies the performance of the proposed method.