In order to improve the identification ability of the fish meal quality detection device,the response characteristic information (10×6) of the fish meal sample is extracted to form the original feature matrix,and the multifeature data fusion optimization of the sensor array is carried out by taking the identification accuracy of the multilayer perceptron neural network as the evaluation index. Firstly,through different normalization processing,the best normalization processing method is obtained. Secondly,1 770 characteristic distances are calculated by factor load analysis results,and 1 770 distances are sorted according to the order from small to large. According to the Euclidean distance between the eigenvalues and the origin,19 eigenvalues with smaller Euclidean distance are eliminated to obtain the highest discriminant accuracy. The original eigenvalues optimized by load analysis are correlated and sorted according to the absolute sum and size of correlation coefficients. When 8 eigenvalues are removed when the absolute sum of correlation coefficients is greater than 37.2,the recognition accuracy is 98.3%,and the feature subset is more compact. The results showed that the characterization characteristics of the sensor signals changed obviously before and after feature optimization. 33 eigenvalues were used to characterize the sensor characteristic signals of fish meal samples. At the same time,the reliability of MLP neural network identification results is explained by Mahalanobis distance,which further explains the rationality of feature optimization method.