Accurate prediction of corrosion state is very important for storage and transportation of oil and gas and safe and reliable operation of chemical equipment. Due to the complex corrosion process and many influencing factors, the prior model in the conventional corrosion prediction method is highly dependent on the environment and the medium and long-term prediction effect is poor. In this paper, a digital analog fusion driven Kalman filter corrosion prediction method integrating fuzzy reasoning and deep learning is proposed. Firstly, based on the long-term corrosion physical model and the actual short-term monitoring data, the fuzzy rules of corrosion velocity were established to obtain the modified corrosion velocity based on the field environment. At the same time, aiming at the prediction lag of fuzzy reasoning results and considering the long-term regularity of corrosion monitoring data, deep learning is used to predict the corrosion rate. Then, the fuzzy strategy and deep learning prediction results are fused to realize the digital analog fusion corrosion prediction based on Kalman filter. Finally, using the actual corrosion monitoring data of natural gas pipelines, this prediction method is compared with GPR, GM, PSOGM, FR, MLP and Kalman filter. The results show that the proposed method has good prediction effect. The relative prediction error of corrosion state in two years is within 1%, the root mean square error is 0. 000 49 mm, and the average absolute percentage error is 0. 34%.