Arranging 3 years’ worth of airborne precipitation particle images to construct a precipitation particle image dataset in Shan⁃ dong Province. Building a precipitation particle recognition model based on EfficientNet convolutional neural network, named PREN (Precipitation particle Recognition model based on EfficientNet convolutional neural Network).The accuracy rate is 98%, and the multimodel and multi-index evaluation and comparison experiments verify that PREN demonstrates excellent robustness and generalization ability. Taking typical stratiform-cumulus mixed cloud precipitation as two examples (total 3 time periods), PREN is applied to the par⁃ ticle characteristics analysis of generating cells. Combined with airborne Ka-band cloud radar and DMT particle measurement system, an analysis conducted on the shape proportion of precipitation particles inside and outside the generating cells and indifferent intensity generating cells, revealing the precipitation mechanism. The results show that the shapes of precipitation particles in the generating cells are mainly spherical, needle-like, irregular and columnar. Precipitation particles outside the generating cells are mostly spherical and needle-like. The cloud microphysical parameters in the generating cells with different intensities vary. The proportion of graupel and needle particles in the precipitation maturity stage is higher than that in the dissipation stage. The average chord length of precipi⁃ tation particles in the maturity stage is 415 µm. While the average chord length of particles in dissipation stage is 367 µm. The par⁃ ticles on the top of generating cells are mainly spherical and hexagonal, primarily growing through the process of deposition. The ratio of irregular particles and columnar particles in the 0 ℃ are increasing, and the melting process and dynamic conditions favor aggregation and growth, forming irregular particles, while columns mainly originate from the upper levels of the atmosphere.