In saxophone performance, the shape of the mouth and the use of breath are crucial factors. However, because these elements are not visually observable, mastering the instrument can be challenging. This study proposes a method for recognizing mouth movements during saxophone playing by focusing on changes in ear canal pressure. Since ear canal pressure varies depending on the positional relationship between the mandible and the ear canal, it serves as an effective non-contact means of measuring mouth movement. We developed a classification model to distinguish four representative types of mouth movements used in saxophone performance. Features were extracted from the pressure data and classified using base models including SVM, KNN, and Random Forest. Logistic regression was applied to integrate the outputs of these classifiers. An evaluation experiment involving five participants achieved classification F-scores ranging from 89.9% to 98.3%, demonstrating the effectiveness of the proposed method. The results of this study provide a foundation for a performance support system that enables saxophonists to objectively recognize their mouth usage.