is proposed. In this method, the large - scale and small - scale features of thecementing quality feature map can be obtained simultaneously by adding a multi - scale convolutional layer, thereby improving thecoverage of the receptive field and enhancing the adaptability of the model to different scales; by embedding the CBAM mechanism,themodel fully extracts useful information for evaluation tasks in two dimensions of space and channel,enhances the model’s ability to focuson features and perception capabilities,and improves the accuracy of evaluation results and its robustness; at the same time,by reducingthe number of network layers,the number of model parameters is reduced,and the computational efficiency and generalization ability ofthe model are improved. The experimental results show that the accuracy rate of the three types of evaluation samples in the test set is
95．86% ,which is about 4. 9 percentage points higher than that of DenseNet-121,and the number of parameters is significantly reduced;compared with BP neural network and support vector machine, it is 9 points higher percent or so. Therefore, it is revealed that theresearch program of implementing the cementing quality evaluation using the improved DenseNet model is not only feasible,but alsosuperior to other similar machine learning methods.