Aiming at the problems of the corresponding features between different modals easy to be fused and mislocated， the subjective empirical parameter adjustment of recognition model experts， and the high computational cost， a self-optimized dual-modal （“contrast enhanced T1 weighting” and “high resolution enhanced T2 weighting”） multi-channel non-deep vestibular schwannoma recognition model was proposed. Firstly， a vestibular schwannoma recognition model was constructed to further explore the multi-modal image features of vestibular schwannoma and the complex nonlinear complementary information among the modals. Then， a model optimization strategy with global parallel sparrow search algorithm based on game theory was designed to realize the adaptive optimization of key hyperparameters of the model， so that the model had a better recognition effect. Experimental results show that compared with the deep learning-based model， the proposed model reduces the number of parameters by 27.9% with an improvement of 4.19 percentage points in recognition accuracy， which verifies the effectiveness and adaptability of the proposed model.

To further improve the efficiency of hyperparameter multi-objective adaptive optimization of deep classification models， a Filter Enhanced Dropout Agent （FEDA） model was proposed. Firstly， a dual-channel Dropout neural network with enhanced point-to-point mutual information constraint was constructed， to enhance the fitting of high-dimensional hyperparameter deep classification model， and the selection of candidate solution sets was accelerated by combining the aggregation solution selection strategy. Secondly， an FEDA model-A novel preference-based dominance Relation for Multi-Objective Evolutionary Algorithm （FEDA-ARMOEA） combined with model management strategy was designed to balance the convergence and diversity of population individuals， and to assist FEDA in improving the efficiency of deep classification model training and hyperparameter self optimization. Comparative experiments were conducted between FEDA-ARMOEA， EDN-ARMOEA （Efficient Dropout neural Network-assisted AR-MOEA）， HeE-MOEA （Heterogeneous Ensemble-based infill criterion for Multi-Objective Evolutionary Algorithm）， and other algorithms. Experimental results show that FEDA-ARMOEA performs well on 41 sets in all 56 sets of testing problems. Experiments on industrial application weld data set MTF and public data set CIFAR-10 show that the accuracy of FEDA-ARMOEA optimized classification model is 96.16% and 93.79%， respectively， and the training time is decreased by 6.94%-47.04% and 4.44%-39.07% compared with the contrast algorithms， respectively. All of them are superior to those of the contrast algorithms， which verifies the effectiveness and generalization of the proposed algorithm.