The task of recognizing the discursive patterns that enable the members of an academic discursive community to achieve the communicative purposes of the academic disciplinary genres is relevant for the processes of incorporation of new members through academic literacy processes. Research on academic genres has focused on expert genres and, to a lesser extent, on genres produced by university students, especially in engineering. The analysis of discursive genre patterns and artificial intelligence (AI), through Natural Language Processing (NLP) have had a complementary development, thanks to both automated classification algorithms and the increased availability of large amounts of textual data, which has enabled the classification of academic discursive genres. The aim of this paper is to automate classification of macromoves (MM) of the meso-genre report of practical experience in engineering. For this purpose, seven traditional classification algorithms, the deep learning model for Spanish called BETO and their corresponding configurations were considered. Among the findings, the best overall performance of SVM_lineal stands out. Also, SVM_linear, BETO and KNN are more effective for the classification of moves in different MMs. These results suggest that the combination of algorithms would be a useful procedure to better classify the macro-purposes of this meso-genre. It is planned to evaluate these algorithms in a tool for-based feedback of written