Abstract: The method for identifying face pressure based on hydraulic support working resistance data needs to address two issues: first, how to extract the cycle-end resistance data from large volumes of working resistance data, and second, how to effectively utilize the extracted cycle-end resistance data to determine whether face pressure is occurring. Most existing methods for extracting cycle-end resistance rely on fixed rules and empirical parameter values, which have low accuracy and poor adaptability in complex working face environments. To address this issue, an intelligent detection method for face pressure identification based on coal cutting cycles was proposed. Coal cutting cycle detection was transformed into a binary classification problem, using a support vector machine (SVM) classifier to intelligently detect the end time of coal cutting cycles, automatically identifying the end of each coal cutting cycle. After obtaining the end times of all coal cutting cycles, the cycle-end resistance data for each support was extracted. Data fusion was performed to generate a single sequence of data that reflects the overall pressure state of the working face. Face pressure identification was then made based on a pressure judgment formula. Experiments were conducted on hydraulic support working resistance data from a working face in a non-contiguous coal mine. The results showed that the proposed method had precision, recall, and F₁ scores of 85.91%, 81.84%, and 83.83%, respectively, for coal cutting cycle detection, and precision, recall, and F₁ scores of 79.43%, 78.76%, and 79.09%, respectively, for face pressure identification These results are superior to the sliding window extreme value method and threshold method, demonstrating significant advantages in cycle-end resistance identification and face pressure judgment.