Accurate retrieval of raindrop size distributions（DSDs）based on dual-polarization radar can provide substantial data for the study of precipitation microphysical properties on a large scale. In order to further im‐ prove DSDs retrieval accuracy，this study proposes a new double-moment normalization method based on the sixth and seventh moments（M6M7 method），comparing it with the third and sixth moments method（M3M6 method）and the constrained Gamma model DSD retrieval method（C-G method）from three perspectives：over‐ all results，different rainfall intensities，and different rainfall particle sizes. Utilizing data from six rainfall events observed by dual-polarization radar and surrounding disdrometers at Heyuan station between May and June 2022，the retrieval results of each algorithm were analyzed. The results demonstrate that during light rain （0 mm·h-1<R≤5 mm·h-1），the M6M7 method exhibits the smallest parameter biases among the three methods. As rainfall intensity increases，biases for most parameters（except for the increase of liquid water content（LWC） and rainfall rate（R））remain relatively stable，with M6M7 consistently showing the lowest biases across different particle sizes and minimal fluctuation with the increase of particle size. Compared with M6M7 method，the M3M6 method incorporates specific differential phase shift on propagation（Kdp）for retrieval. Although Kdp is noise-sensitive，it has good quality in heavy rain（R>30 mm·h-1），resulting in smaller estimation biases for intense rainfall events and a decreasing trend in bias with larger particle sizes（excluding LWC and R）. For moderate rain（5 mm·h-1<R≤30 mm·h-1），the C-G method shows small median deviations yet significant fluctuations in certain parameters. With the increase of rainfall intensity and particle size，its biases shows a trend of first decreasing and then increase，accompanied by pronounced relative bias instability. Comprehensive evaluation results demonstrate that the M6M7 method consistently maintains median deviations approaching 0 across all DSD parameters，while exhibiting significantly tighter error fluctuation ranges. In marked contrast，both the M3M6 method and C-G method display substantially wider bias variability，with their error distributions spanning broader numerical ranges and demonstrating less stable performance characteristics. The newly proposed M6M7 method technique demonstrates advantages over traditional approaches，exhibiting enhanced comprehensive retrieval capabilities with regard to both accuracy and stability，particularly excelling in light-to-moderate rainfall with consistent accuracy. The M3M6 method proves more effective for heavy rain and storms，while the C-G method demonstrates unstable retrieval characteristics. The final section demonstrates the retrieval performance of the algorithm integrating both M6M7 and M3M6 methods，verifying its capability to further improve raindrop size distribution retrieval accuracy.