A total of 410 soil samples were collected in Wuxi, Jiangsu, China, and total nitrogen contents. and soil sample spectra were analyzed indoors. The spectral data underwent preprocessing, including mean centering, standard normal variate transformation, and trend correction. Regression prediction models for soil total nitrogen content were established using partial least squares (PLS), back propagation (BP) neural networks, and one-dimensional convolutional neural networks (1D-CNN). Each model underwent ten-fold cross-validation using datasets preprocessed with various methods, and the average values of the coefficient of determination (R²) and root mean square error (RMSE) were recorded to compare the impact of these three preprocessing methods on model accuracy. The results demonstrated the reliability of the 1D-CNN model constructed based on soil near-infrared spectral data. The R² values for the 1D-CNN model trained with raw data and data preprocessed with mean centering, standard normal variate transformation, and trend correction were 0.907, 0.931, 0.922, and 0.964, respectively. In comparison, the R² values for the PLS model were 0.856, 0.863, 0.861, and 0.880, while the BP neural network model's R² values were 0.874, 0.907, 0.901, and 0.911. The 1D-CNN model consistently outperformed the PLS and BP neural network models on both raw and preprocessed spectral data. Preprocessing the spectral data effectively enhanced the 1D-CNN model's performance, with trend correction demonstrating the most substantial improvement. Hence, 1D-CNN is adept at extracting spectral features and establishing a robust mapping relationship with nitrogen content, effectively preventing overfitting. Even with unprocessed spectral data, it still achieves a commendable level of accuracy.