Abstract: The automation of coal mine production processes has largely relied on artificial intelligence (AI) technology to analyze industrial data. However, AI models developed for single application scenarios prove inadequate for the complex environments in coal mining. Relying solely on distributed computing to process the input features of AI models has led to decreased application efficiency. To address these challenges, an automatic reasoning technology for AI models in coal mine industrial data was developed. The system architecture consisted of three layers: the data layer, the computation-driving layer, and the model reasoning layer. The data layer gathered and stored various types of monitoring data, supplying raw data to the computation-driving layer. The computation-driving layer converted this vast amount of raw data into input features for AI models tailored to coal mining applications. An automatic switching mechanism between two computational engines—based on the input feature values—intelligently selected either Spark-based distributed computing or Python-based local computing, depending on the data volume, thereby resolving the issues of slow processing speeds and high latency in large-scale data applications. In the model reasoning layer, the input features were fed into the AI models for reasoning. A collaborative reasoning mechanism, with multiple triggering methods—scheduled, manual, and feedback-triggered—was introduced to enhance the effectiveness of AI models in complex coal mining scenarios. The results demonstrate that this technology enables rapid calculation of input features for multiple AI models across different application scenarios, as well as fast, automatic, and collaborative reasoning.