As a vital component of coastal zones, the effective analysis of tidal flat areas holds significant implications for economic development and resource utilization. However, due to the influence of surface heterogeneity and meteorological sensitivity in mudflat areas, it is difficult to characterize the true spectral features of mudflat objects. Therefore, there is an urgent need to develop efficient, flexible, and accurate methods for acquiring and predicting spectral radiation characteristics. Based on this, this paper proposed a multi-parameter BP network-based method for radiation characteristics predicting of tidal flat features. Firstly, aiming at the detection requirements of the tidal flat areas, this paper constructed a set of unmanned aerial vehicle (UAV)-borne multi-spectral data acquisition system, in which the spectral channels include 450, 555, 660, 720, 750, and 840 nm, and the 7-element meteorological instrument that records data such as light intensity, temperature and humidity. Secondly, we proposed a spectral data preprocessing method based on standard gray boards, which obtains standardized spectral data through multi-spectral meteorological data, time stamp alignment, and standard gray board correction. Finally, a multi-parameter BP neural network based characteristic prediction method is designed, enabling the prediction of different tidal flat features under the constraints of meteorological conditions such as light intensity, temperature, and humidity. Based on the UAV multi-spectral remote sensing system, this paper collected three types of spectral radiation data including beach forests, beach water edges and sand gravel. The results of spectral data prediction and clustering show that the proposed algorithm can effectively fit the variation law of different spectral radiation values. The minimum MAE, MSE, and RMSE for the prediction of coastal inter-tidal flats reach 0.214 9, 0.184 3, and 0.429 3, respectively, providing reliable data support for remote sensing monitoring and radiation characteristic prediction of tidal flat features.