The research and application of risk analysis and evaluation for underground gas storage facilities are critical due to their diverse equipment, complex process flows, and numerous risk factors. In particular, corrosion failure accidents in ground process pipelines at these facilities have become increasingly common in recent years. Effective and accurate analysis of the causes of these corrosion failures is essential for ensuring the safe operation of underground gas storage facilities. This article presents a risk assessment methodology that leverages data and knowledge fusion. The process begins with a statistical analysis of the corrosion failure data from ground process pipelines in underground gas storage facilities, from which a Bayesian corrosion prediction model is developed. This model serves as the foundation for analyzing the basic events that lead to corrosion failure in these pipelines. Subsequently, a knowledge model of corrosion failure is established, and a detailed analysis of corrosion causes is conducted using the fault tree specific to corrosion failure in ground process pipelines. The importance of each basic event within the fault tree is quantified through the structural importance coefficient assigned to each event. The analysis categorizes the influencing factors of corrosion failure into four main groups. A judgment matrix is then created to determine the relative weight values of these different influencing factors. This matrix is crucial for setting the weight factors in the fuzzy comprehensive evaluation, which ultimately determines the risk level of corrosion failure in ground process pipelines at underground gas storage facilities. By applying examples of corrosion risk assessments for ground process pipelines, this study provides a scientific basis for enhancing safety management and operational practices at underground gas storage facilities.