According to the production characteristics of steam huff and puff in heavy oil wells，this paper establishes a predictive model for the steam injection cycle of heavy oil with the neural network method and the historical production and management data of heavy oil wells in the 4th Member of Eocene Shahejie Formation（Es4）of Block Cao4 in Shengke oil production management area of Shengli Oilfield. The production and full cost of wells for steam injection cyclic of heavy oil are predicted with the proposed model and compared with their historical data of short-and long-term production and full cost.Further，the model is optimized，which enables the multi-dimensional intelligent prediction of steam injection cycle of heavy oil. Moreover，it improves the production prediction accuracy of heavy oil wells，the prediction accuracy of the best time of steam injection cycle of heavy oil，and the preparation efficiency of the best measure scheme for steam injection cycle of heavy oil and enhances the intelligent decision-making，analysis，and management ability of heavy oil wells and the benefit and development level of oil production management areas. Since the technology was popularized and applied in the heavy oil block of Shengke oil production management area in 2021，effective steam injection cycle of heavy oil has been performed for 165 well times in the oil production management area. The cumulative oil increment is 7×104 t in wells for steam injection cyclic of heavy oil. The oil increment is increased by 1×104 t，and the effective oil increase rate is about 17%，compared with those in the same period in 2020.