The computer-aided language teaching system is maturing thanks to the advancement of few-shot learning technologies. In order to support teachers and increase students’ learning efficiency, more computer-aided language teaching systems are being used in teaching and examinations. This study focuses on a multifeature fusion-based evaluation method for oral English learning, completely evaluating specific grammar, and assisting oral learners in improving their oral pronunciation skills. This study proposes an improved method based on HMM a posteriori probability scoring, in which the only standard reference model is no longer used as the basis for scoring and error determination, and instead, the average level of standard pronunciation in the entire corpus is introduced as another judgment basis, based on a preliminary study of speech recognition technology, scoring methods, and relevant theoretical knowledge of information feedback. This strategy can reduce the score limitation caused by standard pronunciation personal differences, lower the system’s misjudgment rate in detecting pronunciation errors, and improve the usefulness of error correction information. An expert opinion database has been created based on the most prevalent forms of spoken pronunciation problems, which can successfully assist learners improve their spoken English level by combining the database’s corrected information. Finally, this study proposes an artificial scoring system for spoken English that performs activities such as identification, scoring, error judgment, and correction opinion feedback, among others. Finally, it has been demonstrated through trials and tests that adding the average pronunciation level to the system improves the system’s scoring performance and has a certain effect on increasing users’ oral pronunciation level.

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With the impact of the epidemic, enterprise risk assessment has gradually become a hot research. At present, enterprise risk assessment is still based on financial data, and there are few studies focusing on text type data. However, text type data has a certain impact on enterprise risks and will affect the overall development trend of enterprises. Therefore, how to build the enterprise risk assessment model and judge the current risk situation according to the text data is the main challenge faced in the current field of enterprise risk assessment. Based on the above problems, the enterprise risk assessment system based on text mining is designed to realize the enterprise risk trend judgment. Focus on the five aspects of the enterprise risk assessment system: first, obtain the corresponding evaluation data according to the enterprise risk assessment task, and build the corresponding database; second, build the enterprise risk assessment field knowledge base to provide knowledge support for the subsequent enterprise risk assessment; third, put forward the named entity identification algorithm for enterprise risk assessment; fourth, propose the relationship extraction algorithm for enterprise risk assessment; fifth, design the enterprise risk assessment model to realize the enterprise risk tendency judgment. Finally, the availability of the enterprise risk assessment system is verified based on the text type data of a particular enterprise.