Weather radars are usually interfered by non-meteorological factors during the observation, resulting in non-meteorological echoes, which will seriously affect the accuracy of the radar"s quantitative precipitation estimation and the performance of short-term precipitation forecasts. This paper uses the scanning observations of C-band Doppler weather radars in Shaanxi (Xi’an, Yan’an, etc.), to construct a quality control method based on the Bayesian classifier and the physical characteristics of the echo: First, the reflectivity factors of precipitation echoes, ground clutter and clear-air echoes of each radar are manually extracted, and based on different types of radar echoes extracted, the reflectivity factor, the horizontal texture of the reflectivity factor, the gradient of the reflectivity factor along the radial direction, the height of the echo top and the vertical gradient of the reflectivity of the different types of radar echoes from 7 radars in Shaanxi are analyzed. And the probability density distribution functions of corresponding characteristics of different types of radar echoes are also analyzed. Then, a Bayesian classifier is established based on the statistical probability density distribution function to initially identify the radar echo. Finally, combined with the physical characteristics of the echo, the sun spike filter, the speckle filter and hole filling are designed to further identify the echo. Using the scanning observations data of 7 radars in Shaanxi Province from July to September 2019, the performance of the radar quality control method is systematically analyzed, and the accuracy of the quality control results is evaluated using the HSS score (Heidke skill score). The results of the radar data quality control method of the provincial business operations were compared and analyzed. The results show that the developed radar quality control method based on Bayesian classifiers and echo physical characteristics can better identify precipitation echoes and non-precipitation echoes, the recognition effect is better than the business results, and the HSS score is 40% higher than the business operation results.