Data security is critical for the healthy development of industrial internet platforms. To scientifically quantify the data security level of these platforms, resilience theory was introduced. Based on the understanding of resilience in the field of system security, the concept of data security resilience for industrial internet platforms was proposed, which included four dimensions, such as risk anticipation, defense, resistance and recovery. Building on this, a data security evaluation index system for industrial internet platforms from a resilience perspective was developed using text mining and semantic network analysis. The G1 method was applied to determine the independent weights of resilience indicators, while the DEMATEL method was used to quantify the interrelationships among these indicators, resulting in hybrid weights. Furthermore, a cloud model-based evaluation model for data security resilience of industrial internet platforms is proposed. This model is applied to assess the data security resilience of the YQ industrial internet platform, with the results aligning with actual conditions, thus validating the scientific and rational basis of the hybrid-weighted and cloud model-based evaluation method. Finally, recommendations are made for the future development of data security resilience in industrial internet platforms, providing theoretical support for enhancing data security resilience moving forward.