This study seeks to enhance the effectiveness and reliability of virtual test simulation models for flight control systems. To address the challenges posed by multivariate redundancy and strong data coupling in virtual testing, a simulation model validation method based on multivariate data fusion is proposed. Initially, multivariate simulation data is integrated to minimize noise and reduce errors. Subsequently, the Pearson correlation coefficient is applied to assess the correlations among the multivariate output data, while the TCN-LSTM approach is utilized to fuse data features, thereby uncovering the spatio-temporal relationships within the data. Finally, probability distribution analysis is conducted to quantify the discrepancies between simulation and reference data, which are then converted into credibility scores to facilitate the effective validation of the simulation model. Experimental results indicate that 80% of the output data satisfies the simulation model credibility criteria, with 50% of the output data classified as fully credible. These findings underscore the method's ability to significantly enhance the accuracy and reliability of simulation model validation, while effectively integrating the quantitative outcomes as required.