Abstract: Due to the low-porosity and low-permeability characteristics of tight sandstone gas reservoirs, the gas-water distribution is affected by the coupling of multiple factors. well liquid loading is a common issue in gas wells, which leads to a significant increase in bottom-hole back pressure and severely restricts the stable production of gas wells. However, existing well liquid loading diagnosis methods have bottlenecks such as single diagnostic indicators and insufficient accuracy, so there is an urgent need to develop a precise diagnosis technology.In this study, first, by comparing and analyzing the production performance data, test data, water production data, and other information of multiple gas wells in the same block, the data quality was improved through data cleaning, denoising, and outlier correction. Then, seven diagnostic methods were integrated to establish a liquid loading diagnosis and calculation model from multiple dimensions including pressure, flow rate, and water cut. Finally, relying on a software platform, research on big data analysis and intelligent identification logic was carried out to realize precise identification of liquid loading, quantitative determination, liquid loading early warning, and multi-dimensional dynamic tracking.This technology integrates multiple methods to construct an identification model and combines multi-variable dynamic big data analysis. The field application has achieved remarkable results: An in-depth analysis was conducted on the liquid loading diagnosis data of 22 tight gas wells in the study block. The accuracy rate of the comprehensive method in judging the existence of liquid loading reached 90.9%, which was 18.2 to 36.4 percentage points higher than that of single diagnostic methods. In terms of the accuracy rate for the degree of liquid loading, the comprehensive method achieved 72.7%, representing an improvement of 13.6 to 31.8 percentage points compared with single methods. It not only realizes batch, rapid, and accurate analysis of liquid loading but also can capture the dynamic changes of liquid loading in real time. The research and application of this technology provide a highly valuable reference for tight gas fields and show a good prospect of popularization and application.