【Objective】 This study constructed a deep learning-based crown width model for Cunninghamia lanceolata plantations in the Jiangle region, aiming to analyze the contributions of tree size, site quality, stand structure, and competition to crown width prediction. 【Method】Six deep neural network (DNN) models were developed, and the shapley additive explanations (SHAP) interpreter was employed to analyze the feature importance of each variable. Additionally, a generalized crown width model was built and compared with the DNN model that utilized the same set of optimally selected variables based on correlation coefficients, in order to validate the reliability of the DNN approach. 【Result】The constructed DNN models exhibited no overfitting, with stable root mean square error (RMSE) and mean absolute error (MAE) values in 10-fold cross-validation. The DNN model incorporating all 13 variables (DNN5) achieved the highest performance, with an R² of 0.60. However, models with six input variables (DNN3 and DNN4) yielded R² values of 0.58 and 0.57, respectively, which better meet practical application needs. The DNN6 model, constructed using variables selected based on their correlation coefficients, achieved an R² of 0.54, outperforming the generalized crown width model (R²= 0.46) that used the same variables. The SHAP contribution value of DBH was the highest. Additionally, in the feature ranking, basal area (BA), stand density index (SDI), McIntosh evenness of diameter class (Emi), and Gini coefficient (GC) held important positions. The inclusion of both types of variables significantly improved prediction the accuracy of the crown width model. 【Conclusion】The results demonstrate that the constructed DNN model effectively predicted the crown width of C. lanceolata in the study area, indicating that deep learning holds significant potential for crown width modeling.