For improve the evaluation ability of eco-environmental quality in rural tourism areas, a factor evaluation method was proposed. The big data mining method is used to quantitatively evaluate different types of ecological environment and extract their features. The fuzzy clustering method is used to adaptively schedule the extracted feature quantity, and the evaluation objective function is constructed by combining the joint association rule mining and linear fitting analysis. The main indicators of tourist attractions are graded by factor evaluation method, and the ecological environment quality of tourist attractions is evaluated by objective function. A dataset consisting of 800 evaluation
samples and 1,000 training samples, which spans from July 2016 to April 2022, was used in simulation experiments performed with MATLAB and Visual C++ to verify the efficacy of the proposed method. While evaluating the quality of the eco-environment, descriptive statistical analysis shows better accuracy and balance than conventional methods. Based on the results, the proposed approach offers a confidence level greater than 91% while achieving higher evaluation levels at a lower cost. The proposed method is reliable and cost-effective, as evidenced by life cycle cost comparisons between various improvement schemes. This is especially true given the reduced expenses associated with operation and maintenance. The method's accuracy and resilience in assessing the eco-environmental quality of rural tourism regions are highlighted by the much decreased steady-state errors and average risk values found in the analysis of experimental data.