Pressure pipes play an important role in industry, but due to their long service life and the influence of external environment, the reliability and service life of pipelines have become a key issue. Traditional reliability analysis methods are usually based on experience and statistics, which lack accuracy and timeliness. For this reason, this project proposes to study this using artificial intelligence techniques. In this paper, a lot of data are analyzed from the aspects of pipeline running state, environmental conditions, maintenance and so on. On this basis, the method of artificial neural network is used to train the obtained data and simulate the reliability of the pipeline. On this basis, a new reliability calculation method for pipeline structure is proposed. In addition, we will also conduct a deeper study on the algorithm to improve the accuracy and generalization ability of the model. This method can effectively identify many laws and regulations hidden in many pipelines, and improve the reliability and durability of pipelines. The experimental results show that the proposed algorithm is correct. The results show that the application of artificial intelligence technology in high-pressure pipeline analysis has obvious advantages in accuracy and reliability. The research results of this project will provide theoretical basis for improving the safe and reliable operation of high-pressure hose, and can be extended to other industrial equipment.