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This study applies big data processing technology and parallel computing methods to assess the teaching effect of Japanese in the Flipped Classroom (FC) and task-based teaching mode. We propose a model for evaluating the Japanese teaching effect in this mode using feature offset compensation. We employ distributed mining of association rules to detect the teaching effect and extract ontology information and association rules related to the distribution of Japanese teaching effect in the FC and task-based teaching mode. Furthermore, we construct a fuzzy decision-making model for evaluating the teaching effect. The joint information entropy characteristic value of the teaching effect deviation is calculated, and the extracted characteristic quantity is classified and identified using feature deviation compensation and the C-means clustering method. Based on the classification and identification results, we achieve accurate evaluation of the Japanese teaching effect in the FC and task-based teaching mode. Our simulation results demonstrate high precision rates and confidence levels in the evaluation of Japanese teaching effect in this mode.