In the few-shot recognition scenario of space targets observed at low frequency, the drastic changes in the image representation of space targets in different poses challenges to the extraction of discriminative features and the correlation of features between images. To address these issues, the few-shot space target recognition method based on adaptive cross fusion of local features was proposed. Based on the existing few-shot learning framework, the feature cross fusion module based on self-attention and cross-attention was used to adaptively learn the correlation between local features, improve the discriminant and robustness of feature in different poses, effectively explore the similarity between the support set and the query set, and improve the accuracy of feature association with representation differences. Meanwhile, the sample label weight based on neighborhood density was employed into the loss function to solve the learning bias problem of the network model caused by unbalanced space target datasets. Through the verification on different datasets, the proposed method is proved to achieve higher recognition accuracy．