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In the process of aircraft structure X-ray images evaluation，there are some problems such as inaccurate crack segmentation and difficult crack detection under complex background. An intelligent evaluation model （ELAN-Seg） for X-ray crack images of aircraft structures is proposed based on efficient layer aggregation network. The ELAN-Seg model is compared with the DeepLabv3+ model in the crack segmentation ability of the Xray images. Combined with image processing technology，the crack length of model segmentation is evaluated，and the model is verified by X-ray images acquired during aircraft strength test and field maintenance. The results show that the segmented minimum crack length is about 3 mm，the ELAN-Seg model is more accurate in crack segmentation of complex background X-ray images，and the crack detection rate is less than 3.8%. The proposed model has engineering applicability.

In view of the problem that the existing air combat decision-making methods do not have an complete consideration for multi-fighter air combat, in the context of air combat before contacting enemy, this paper improves the existing methods from three aspects. The substitutes the combat power field for superiority functions. This reflects the influence of equipment performance on decition-making, second, it achieves preliminary target assignment based on giving full play to the multi-target attack abiliy and improving cooperative efficiency. Third, it introduces the coefficient of target importance and yield, aiming to take the multi-objective processing capability of radar and the rationality of attack resources allocation into consideration Finally, tt solves the index function based on the extrapolation and particle swarm optimization simulation result proves the effectiveness of the improved content The result of research shows that the improved decision-making method is more consistent wtth the ar combat reality. It also has a better cooperative efficiency than the method before improvement [ABSTRACT FROM AUTHOR]