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A novel algorithm named as task-aligned object detection algorithm based on random scaling mixture and cross-scale feature enhancement is proposed to avoid the disadvantages of poor robustness, loss of some semantic information in the top-level feature layer of the feature pyramid network, and existing the semantic gap for the feature layers with different scales in the TOOD algorithm. After utilizing the 2x4 hybrid augmentation method to enrich the training samples, the presented method improves generalization and robustness of the model. In order to reduce the loss of the semantic information at the highest level, the multiple residual feature enhancement module is constructed by adaptively fusing the context information with different scales at the top level. Moreover, the proposed method constructs the stacked pyramid convolution module, which reduces the semantic gap among the different scale features and improves the effect of multi-scale feature fusion. The experimental results show that the mean average precision, the precision and the recall of the proposed algorithm on the Pascal VOC dataset are 3.76 %, 15.71 % and 6.28 % respectively higher than the TOOD algorithm. Compared with the state-of-the-art algorithms, the presented algorithm achieves higher F1 value and mean average precision.