The success of Artificial Intelligence has attracted researchers from different areas to use computational techniques in tasks of extracting knowledge from unstructured data, such as textual documents, presenting itself as a possible solution for the classification of Specialized Knowledge of Physics Teachers , which consists of an analytical tool that describes the Knowledge of Physics (PK) and Didactic knowledge of Content (PCK), considered very important to assist in the identification and analysis of knowledge mobilized by teachers in their teaching practices. However, the task of identifying and classifying knowledge present in textual documents presents some challenges, such as: investigating textual documents is laborious, time-consuming and involves the labor of specialized people. In this sense, the objective of the research is to analyze the effectiveness of the algorithms used in the automatic classification of Expert Knowledge of Physics Teachers (PTSK) in texts from a previously classified database. The methodological approach is quantitative, exploratory and applied to predict the class Knowledge of Physics (PK) or Didactic Knowledge of Content (PCK) to characterize knowledge. For this, two algorithms were used: doc2vec and J48 and the results were analyzed based on the results achieved in the validation metrics. The best result was achieved with doc2vec, obtaining an 88% success rate. Based on the results achieved, it can be concluded that the strategy of using artificial intelligence for the automatic classification of knowledge of Physics teachers is a plausible solution.