COVID-19 created difficulties and problems in almost everyone's daily life routine. Educational institutions too had to reschedule their academic activities. This shift caused attitudinal and behavioral changes in students' learning patterns. Using stress theory, the present study tries to determine the association of fear of COVID-19 with students' performance. In addition, the present study also attempts to check the impact of fear of COVID-19 on anxiety. Further, this study tries to find the association of anxiety with students' performance. This study also attempts to determine the mediating role of anxiety and the moderating role of mindfulness. For empirical investigation, the current study collected data from 320 HSK students from different colleges and universities in China. The present study applied partial least square structural equation modeling for the empirical investigation of hypotheses by using Smart-PLS software. The present study's findings confirmed that fear of COVID-19 negatively affects students' performance, and it positively correlates with anxiety. The study's outcomes revealed that anxiety negatively affects students' performance. The outcomes also confirmed that anxiety negatively mediates the relationship between fear of COVID-19 and students' performance. The present study's findings acknowledged that mindfulness does not moderate the relationship between fear of COVID-19 and student performance and has a positive moderation between anxiety and student performance. The present study offers important practical, theoretical, and managerial implications.

This paper deals with the problem of recognizing associated words in compound sentences. As the conventional methods rely on manually extracted utterance features, in this paper, an associated words recognition method based on neural networks is proposed. The proposed method fuses the features extracted from the compound sentence corpus into word vectors, which are fed into the constructed deep neural network model for training. To analyze the compound sentences in the modern Chinese compound sentence corpus, we extract four common utterance features to establish an utterance feature base. Then, the extract features from the utterance feature base are combined into a new feature, which possesses high discriminative ability. Based on the utterance feature base, a training set and test set is constructed for test the performance of the proposed method. Experimental results show that the proposed method both improves the efficiency of recognition and achieves a high correct rate with respect to the traditional method.