The geological complexity, environmental sensitivity, and ecological fragility inherent in complex and difficult areas (CDAs) present new opportunities and challenges for technological innovation in railway engineering development in China. At the current stage in China, the process of technological innovation in railway engineering within CDAs still faces a series of pressing issues that need addressing. The paper identifies and determines 22 influencing factors for technological innovation in railway engineering within CDAs across five dimensions. Subsequently, a technological innovation model for railway engineering in such areas is constructed based on system dynamics (SD), which is followed by simulation and sensitivity analysis to identify the key influencing factors. The results indicate that key influencing factors for technological innovation in railway engineering within CDAs include technological innovation capability, the adaptability of technology to the environment, R&D funding investment, technological product requirements, technological innovation incentive mechanisms, and the level of technological development. The importance ranking of each dimension is as follows: technological factors > technical factors > management factors > resource factors > environmental factors. The paper provides new insights for promoting technological innovation and management development in complex and challenging railway engineering projects. It offers a fresh perspective to enhance the technological innovation efficiency of railway projects in complex and challenging areas.

Extreme climatic conditions and active geological factors posed challenges in evaluating disaster risk trends in railway construction projects and identifying key influencing factors. Traditional technology is difficult to adapt to the exploration and unknown construction process of railway engineering in complex and difficult areas. Therefore, there is an urgent need for technological innovation. The study used the Vague set theory method to screen and determine a list of risk factors for railway engineering technology innovation in complex areas, including 5 primary risk indicators and 26 secondary risk indicators. Based on this, an evaluation system for risk factors of railway engineering technology innovation in complex areas was established. Secondly, this study combined the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) with a cloud model to comprehensively evaluate risks and create cloud maps. Based on the calculation results, the risk level of railway engineering technology innovation risk factors in complex areas is obtained as environmental factors > technological factors > social factors > management factors > resource factors. The “combination weighting-cloud model” framework adopted in this study effectively overcomes the problem of insufficient representation of traditional single weighting method by integrating subjective and objective weight optimization and dynamic risk coupling analysis and significantly improves the multidimensional adaptability and dynamic evaluation accuracy.