In this paper,a new residual network bone age assessment model combin ed with high-efficiency channel attention module was proposed to address the pr o blems of small data size,uneven sample distribution and low accuracy of existin g methods for bone age assessment.Firstly,the deep residual network is selecte d as the basic convolutional neural network model,the convolution efficiency is improved by combining the high-efficiency channel attention module,and the lo s s function is improved to alleviate the influence of the uneven distribution of samples.Then,the model in this paper is pre-trained on the ImageNet data set t o obtain the basic feature expression of the image.Finally,the open data set w as fine-tuned with the random depth algorithm,and the accuracy of bone age ass e ssment was obtained by cross validation.The results showed that the mean absolu te error of this method on RSNA and DHA data sets was 4.69months and 5.98month s,respectively.When the tolerance was 12months,the accuracy of bone age asse ssment was 98.36% and 94.88%.This indicates that this paper can significantly i mprove the accuracy of bone age assessment,alleviate the impact of small data s ize and uneven data distribution to a certain extent,and restrain overfitting w hile improving network learning ability.