The development of tight gas reservoirs is restricted by low permeability, strong heterogeneity and complex fluid characteristics, and the accurate evaluation of the estimated ultimate recovery ratio (EUR) faces the challenges of poor applicability and high uncertainty. This paper aims to systematically sort out the technical bottlenecks of the existing EUR evaluation methods, propose targeted improvement strategies, and discuss the potential breakthrough direction under the integration of big data and artificial intelligence, so as to provide theoretical guidance for the economic and efficient development of tight gas reservoirs. Using dynamic production data analysis and decline curve fitting, a modified model of traditional reserve evaluation method is established; the machine learning algorithm is introduced to construct an intelligent prediction model of EUR by integrating logging, fracturing and production data. Combined with Monte Carlo simulation, EUR uncertainty quantification and risk classification are carried out. The research shows that the EUR evaluation of tight gas reservoirs needs to break through the linear assumption of traditional methods, and improve the prediction reliability through multi-parameter collaborative modeling and data-driven technology of geological engineering. At present, the main challenges are the accurate characterization of fracture network, the definition of dynamic threshold of economic limit production and the standardization of multi-source data. The research results provide a technical path for the innovation of tight gas reservoir reserve evaluation system and the practice of reducing cost and increasing efficiency.