Prediction based on multi - model agricultural assistance recommendation algorithms

Against the backdrop of a prominent information asymmetry problem in the agricultural product market, algorithms for promoting agricultural product sales are of great significance. However, they face challenges such as poor data quality. This study aims to improve the algorithm performance. By optimizing the Latin hypercube sampling method, a distance - metric - based strategy is introduced to optimize the distribution of sampling points and set adaptive parameters, enhancing the uniformity and representativeness of samples. A prediction model is constructed by combining the powerful non - linear mapping and self - learning capabilities of the BP neural network. The research obtains data from multiple channels, conducts sampling and feature extraction after pre - processing, sets network parameters to train the model, and then evaluates it.