With the development and popularity of information and network technologies, online learning, which can meet learners’ individual needs to a greater extent, has attracted more and more learners because it creates a rich and diverse educational environment that allows learners to access information, experience learning, interact with peers and teachers, and participate in campus-wide co-curricular programs, and foreign language learners around the world occupy a high percentage of the online learner population. This hybrid technology combines existing language teaching evaluation models, takes advantage of data from online education, and creates corresponding criteria through machine learning fuzzy algorithms and large data sample training, combined with the theory of effective teaching evaluation, which is beneficial for all participants of online Chinese listening and speaking teaching to improve their learning effectiveness. The system can help learners to identify and determine the types of errors in Chinese listening and speaking learning in a timely manner and make a more objective and comprehensive evaluation of learning performance at the same time, it helps teachers to trace the effectiveness of teaching design and implementation in a targeted manner and make corresponding scientific decisions. This paper introduces a hybrid technique based on fuzzy evaluation method, for determining and suggesting possible types of errors in international Chinese online listening and speaking instruction and giving suggestions for improvement. When we focus discussion on international Chinese teaching, which has been developed for a relatively short time and not experienced enough, online teaching effectiveness evaluation has become an important obstacle to the development of teaching. With the increasing popularity of online foreign language teaching and learning practice, learners and teachers have developed a high demand for evaluation of teaching effectiveness.
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