Traditional production rate forecasting methods are often limited by the production history of individual wells and assumptions of the models, leading to unquantified uncertainties in the prediction results and difficulty in considering the guidance of development patterns from other wells in the block on the target well. Additionally, they fail to fully utilize a large amount of relevant production history data. To address these issues, a new model for probabilistic production rate forecasting driven by multi-well production data is proposed, based on deep autoregressive neural networks. This model integrates dynamic covariate data such as production time and tubing/casing pressure, and employs Bayesian inference along with gradient descent and maximum likelihood estimation methods to derive a generalized historical-future production probability evolution pattern shared among multiple wells. Through data-driven approaches, it achieves probabilistic production forecasting for multiple wells. The performance of the deep autoregressive neural network model is studied using data from 943 tight gas wells in two blocks in the Ordos Basin. Results indicate that compared to traditional deep learning models like LSTM, the new model combines the learned generalized production probability evolution pattern with specific production history data of the target well, forming a “generalized + specific” production probability prediction method. On average, it reduces the relative error by 45% compared to the LSTM model. The classification model reduces the relative error by 24% compared to the global model, further reducing the uncertainty of probability prediction based on the global model and improving the prediction accuracy of specific fine-classified wells. Through validation with actual data, the new model demonstrates better prediction accuracy and stronger robustness, making it applicable for multi-well production forecasting analysis in oil and gas reservoirs.