To increase the accuracy of predicting the operation level of agricultural mechanization in China, this study establishes a wavelet-BP neural network prediction model by targeting the nonlinearity and non-stationary features of the data under the fundamental principle of wavelet analysis and BP neural network. First, the major factors that influence the operation level of agricultural mechanization are determined and analyzed, and dimensionality is reduced through a principal component analysis. Second, the time series of the operation level of agricultural mechanization and the principal component series of the influencing factors are decomposed to obtain low-frequency and high-frequency components. A BP neural network prediction model is built for the low- and high-frequency components. Lastly, the obtained low-frequency and high-frequency components are examined through linear superposition, and the final prediction results are obtained. The proposed method is verified by predicting the operation level of agricultural mechanization in China. Results show that the wavelet-BP neural network prediction model can perform accurate prediction. The model evaluation indices, namely, average relative error, root-mean-square error, Theil IC, consistency indicator, effective coefficient, and excellence rate, are 0.44%, 0.293, 0.002 4, 0.90, 0.972 7, and 100%, respectively; these indices are superior to those of conventional and other models. The research findings can serve as a theoretical basis for the formulation of relevant agricultural mechanization policies and laws in China.