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In this paper, a hybrid weight design and optimization mechanism is proposed to ensure a better application effect in the multi-level weighted evaluation/optimization process of intelligent neural network (NN) guidance algorithm. According to different target tracking scenarios, the tracking index are divided into different periods, a single period score is given according to a linear-nonlinear hybrid scoring mechanism. Furthermore, in a single index, the internal scores of different time periods are weighted and fused for the second time to obtain the index overall score. Finally, the third weighted fusion of the multi-index scores is used to obtain the comprehensive score of the algorithm. Design the algorithm parameter evaluation case sets, repeat the above compound weighting, obtain the case with the highest comprehensive score. Finally, the algorithm is optimized by the highest score case. In the hybrid weight design mechanism, the objective grey correlation first defines the weight range, then the subjective expert decision method with local extreme expert elimination and standard deviation global test for double optimization finally ensures the weight reliability of the hierarchical evaluation optimization. The evaluation optimization experiment using fuzzy NN radar seeker verifies the effectiveness of the method. [ABSTRACT FROM AUTHOR]