Multi-focus image fusion is an important branch in the field of computer vision. It aims to use image processing technology to fuse the clear regions of multiple images focusing on different objects in the same scene, and finally obtain a fully clear fusion result. With the breakthrough of machine learning theory represented by deep learning, convolutional neural network is widely used in the field of multi-focus image fusion. However, most methods only focus on the improvement of network structure, and use simple one-by-one serial fusion method, which reduces the efficiency of multiple image fusion, and the defocus spread effect in the fusion process also seriously affects the quality of fusion results. In the application of microscopic imaging analysis, we proposed a maximum spatial frequency in feature map (MSFIFM) fusion strategy. By adding a post-processing module in the convolution neural network based on unsupervised learning, the redundant feature extraction process in the one-by-one serial fusion is avoided. Experiments shown that this strategy significantly improves the multi-focus image fusion efficiency with multiple images. In addition, we presented a correction strategy, which can effectively alleviate the effect of defocus spread on the fusion result.