With the continuous development of remote sensing technology,it is widely used in the fields of map drawing,resource exploration and disaster early-warning.Remote sensing target detection is the key step of remote sensing image interpretation.In the process of detecting remote sensing targets,the traditional detection algorithm has some deficiencies,such as target missing,low detection accuracy and inability to detect small target.A remote sensing target detection algorithm based on Multi-Scale Feature Enhancement Convolution Neural Networks (MSFE-CNNs) is proposed.By enhancing and fusing the features of different convolution layers,the model has faster training speed and higher detection accuracy.The proposed algorithm combines feature extraction module,feature enhancement module,self-attention mechanism and pyramid feature attention mechanism.The feature extraction module extracts features from the input of massive remote sensing data to obtain multi-scale features of different types of targets.The feature enhancement module is used for enhancing the correlation of features of different convolution layers and strengthening the learning ability of the model and the nonlinear relationship between features.Self-attention mechanism and pyramid feature attention mechanism mainly solve the problem that traditional convolutional neural network can not obtain the features of small-scale targets.To verify the effectiveness of the proposed algorithm,different algorithms are compared on DOTA data sets.Experimental results show that the proposed algorithm is superior to the existing target detection algorithms based on deep learning in both detection accuracy and training speed.