Plunger gas lift is one of the most applicable techniques in solving two critical problems of low-production gas wells: fluid accumulation and intermittent production. It has been applied on a large scale in the gas field. However, the manual daily data analysis, production process adjustments, and effect tracings bring a heavy workload and cause problems such as improper manual production process adjustment time, increasing load factor, and plunger position failure. It will be an inevitable trend for the plunger lift technique to innovate into a mature, scable, used, and intelligent one. Neural network algorithms have been widely applied in artificial intelligence, economics, military industry, etc. It can obtain the internal hidden working principles through calculations of various parameters and use the learned working principles to predict future working conditions. The characteristics of neural network algorithms can solve the dilemmas and problems faced by the plunger lift operations. The Sulige gas wells are highly heterogeneous, and the production processes of each gas wells are different. However, there is still a relatively fixed production process for each specific gas well for a certain period. That is, when opening/closing the gas well, the well’s production status tends to be the same under the same parameters. So, the artificial intelligent neural network algorithm can study the plunger lift dynamic production data, build the well’s dynamic model, use the dynamic model to predict the well’s future production status, and automatically find the best process to realize the well’s maximum gas production. Based on plunger load factor, functioning status, etc., the system will further optimize the production process, finally output the scientific and highly efficient production process, ss, and then put it into applications. The model building has been successfully finished, and the intelligent neural network system has been installed on 11 plunger lift wells. The system works steadily and brings sound implementation effects. Through the neutral network algorithm’s intelligent parameter adjusting, the well’s production rose by 36% compared to the production under manual parameter adjusting conditions.