According to the features of stratification and obvious inhomogeneity in geological soil in Huaibei plain, BP neural network prediction method for stratification and bearing capacity calculation of multiple cross-bedded foundation was proposed. By comparing the results of drill sampling, static cone penetration tests and screw plate tests, plate loading tests, penetration resistance ps value was found as an evaluation index for stratification and bearing capacity prediction of cross-bedded foundation. Moreover, gradient descent algorithm and conjugate gradient algorithm BP neural network models were obtained, and the calculation results of the two algorithms were comparatively analyzed. The results show that penetration resistance value can be taken as an evaluation index for stratification and bearing capacity prediction of cross-bedded foundation in Huaibei plain. Gradient descent algorithm and conjugate gradient algorithm BP neural network models have good results for soil identification and bearing capacity determination, which can meet the accuracy requirements of actual engineering. However, the computational efficiency of gradient descent algorithm is significantly lower than that of conjugate gradient algorithm.