In order to achieve real-time evaluation of the surface microhardness of materials during laser shock peening， an online monitoring method for the surface hardness of 7075 aluminum alloy combining acoustic emission technology and machine learning technology is proposed. Firstly， a comprehensive metric to characterize the surface hardening of material， i.e. the sub-surface hardening rate， is constructed through offline hardness testing； secondly， the anti-symmetric A0 mode-based Mel cepstrum time-frequency map feature extraction is implemented using modal acoustic emission theory； then， a neural network quality assessment model incorporating multiple sensory fields and attention mechanisms is established； finally， the validity and feasibility of the proposed method are verified by the measured data of laser shock peening. The experimental results show that the proposed time-frequency map features are richer in detail information， and the proposed model achieves the highest average accuracy of 97.41% compared with the traditional neural network.