Abstract: Using 82892 volume scanning data at 0.5° elevation angle from the S-band dual polarization radar deployed in Guangzhou (CINRAD/SAD) and 538560 1-minute rainfall data from 1109 stations within the radar's 100 km detection range from 2018 to 2020, three deep learning networks（Z-Rnet, K_DP-Rnet and Pol-Rnet）are designed for radar quantitative precipitation estimation (QPE) based on single and three radar moments, respectively. Furthermore, based on the three networks and with K_DP = 0.5°/km as the threshold to divide the training dataset as heavy, light, and all rain data, a total of 9 QPE models are built. On the basis of using the common mean square error as the loss function, a self-defined loss function is proposed by adjusting the weight for different precipitation intensity. Several indexes including ratio deviation, relative deviation, mean square error (MSE), mean absolute error (MAE) and mean relative error (MRE) are then used to evaluate the performance of the models. Finally, three precipitation processes that are respectively dominated by cumulus-stratiform mixed, convective and stratiform clouds are used to test the effect of QPE. The results suggest that the models fitted by deep learning have better QPE results, and the QPE accuracy for the data that are divided into heavy and light rain is better than that for the data that includes all types of rainfall. The MSE, MAE and MRE with the self-defined loss function are improved by 8.62%, 12.52%, 16.34% than that with the traditional mean square error loss function. Among them, the QPE with Pol-Rnet, i.e., Z_H, Z_DR and K_DP are used as input factors, is the best, and the above indexes are respectively increased by 6.82%, 8.43%, 7.22% than that with Z-Rnet, and by 12.33%, 17.61%, 17.26% than that with K_DP-Rnet.