Abstract: Aiming at the problems such as the infrared weak flying targets being not significant，and the difficulty of designing the artificial feature extractor，an infrared dim flying target recognition algorithm was proposed based on deep learning.Similarly，background interference could also be a major problem.Therefore，a detection framework was established to deal with the mentioned problems based on the YOLOv4 model.K-means++ algorithm was used to cluster the candidate frames of the training set.This framework was demonstrated to select the sufficient size of anchor，and it was more reasonable to select one specific sample from the database as the initial center other than picking the initial point randomly.At the same time，the convolutional attention module was introduced into the framework，which not only made a more reasonabe allocation of algorithm resources but also made the information more sensitive in detecting the infrared weak flying targets.Since the spatial pyramid pooling module was well enhanced，more original information about the photograph could be retained by using the average pooling method，which could reduce the effect caused by noise and dead pixels within the space-based imaging process.The experiential simulation illustrates the accuracy can reach 80.13% when calculating the anchor size based on K-means++ modulus.The recognition of the algorithm could even reach 83.3% if adding SPP and CBAM modules to the test set.Furthermore，the accuracy of detecting the infrared weak small flying targets also gains an exceptional improvement after the adjustment on modulus.

Key words: deep learning, convolutional network, infrared dim small target, space based surveillance, object detection