In view of the fact that the quality of post-grouting of pile ends in sand layers in Yangon is affected by complex geological conditions (high permeability and heterogeneity) and the low efficiency, high cost and difficulty in dynamic evaluation of detection methods such as artificial coring and static load tests, this study proposes an intelligent detection and evaluation system for the quality of post-grouting of pile ends based on multi-source data fusion and deep learning. First, this paper introduces the grouting methods and grouting methods in sand layer areas. Secondly, a three-dimensional dynamic diffusion model of sand layer grouting is constructed to quantify the coupling relationship between grouting pressure-diffusion radius-soil strength. Then, a lightweight convolutional neural network (CNN) and long short-term memory network (LSTM) fusion algorithm is designed to realize intelligent classification and abnormal location of grouting uniformity and density. Finally, a quantitative evaluation report is generated based on the data collected by the multi-sensor collaborative acquisition module to the visualization evaluation platform. The experimental results show that the accuracy of the system in grouting quality detection is 92%, which is significantly higher than the traditional static load test (85%) and manual coring (80%). In terms of detection efficiency, the intelligent detection system only takes about 1 h to complete, while the traditional methods take about 3 days and 5 days, respectively. In addition, the intelligent detection system has significant advantages in cost control and has a low overall cost. The experiment verifies the effectiveness and superiority of the system in complex geological environments, and provides strong technical support for the dynamic evaluation of the quality of post-grouting at the pile end.