Data-driven soft-sensing modeling plays a critical role in process industries, yet faces challenges from heterogeneous noise contamination and the coexistence of linear and nonlinear correlations in industrial datasets. These issues significantly compromise model prediction accuracy. To address this, we propose a fused correlation-based collaborative shared noise algorithm for robust soft-sensing modeling. The algorithm integrates Pearson correlation coefficients (linear relationships) and Spearman rank correlation coefficients (nonlinear relationships) to compute data credibility, thereby optimizing noise allocation under mixed correlation conditions. A convolutional neural network (CNN) is subsequently employed to construct the soft-sensing model. Experiments on a debutanizer column dataset demonstrate the superiority of the proposed method. The FC-CSNA outperforms baseline denoising techniques, including wavelet transform, denoising autoencoders, and the original collaborative shared noise algorithm, in noise suppression. The hybrid model achieves state-of-the-art prediction performance, with an R2score of 0.971 6 and mean squared error (MSE) of 0.001 1, validating its effectiveness in handling industrial data complexity.