1. Research background

Introduced the research background of the semantic network structure of second language vocabulary for Chinese English learners. The research mainly adopts two paradigms: corpus analysis and vocabulary association testing. The former reveals the non-linear characteristics of vocabulary semantic development, while the latter reflects the association and interaction of vocabulary in the psychological lexicon. Network science provides a new perspective for the study of vocabulary representation and acquisition, viewing words as nodes and semantic relationships as connections to construct a lexical semantic network. Research has found that the semantic network structure of second language vocabulary becomes more complete with the increase of vocabulary, but the relationship between community level and learning time, as well as the role of central words, still needs to be studied. This study aims to compare the differences in the semantic network of second language vocabulary between middle and high school learners at macro, meso, and micro levels, and explore the impact of learning time on network structure.

2. Research methods

This paper introduces a comparative study method for the semantic network structure of second language vocabulary among Chinese middle and high school English learners. The research subjects are 100 middle school and high school students, with different ages, English learning time, and vocabulary. Using the semantic fluency test method to collect data, participants are required to write as many English words as possible within one minute that belong to the category of "profession". Prior to testing, a pilot experiment was conducted to ensure that the selected semantic categories met the testing requirements. In the study, the strength of semantic connections was determined by measuring the distance between words produced through semantic fluency testing, and a vocabulary vocabulary relationship matrix was established. Use Ucinet tool to process matrices and draw network diagrams, analyze the macro structure of the network through parameters such as size, density, diameter, average path length, average weighting, central potential, and small world index. At the meso level, modularity is used to measure community structure, while at the micro level, centrality is focused on individual nodes.

3. Results

By analyzing the vocabulary produced by middle and high school English learners in semantic fluency tests, two sets of vocabulary vocabulary relationship matrices were constructed, and a visualization of the vocabulary semantic network was further generated. Research has found that the vocabulary semantic network in the high school group has a larger scale and richer and more complete representations, while the network center and edge boundaries in the middle school group are more distinct, indicating a stronger representation of category centers than edges. Both networks are small world networks, but the high school group has stronger small world features. The network scale of the high school group has expanded significantly, but the semantic connection density is lower. In addition, the decrease in network centrality in the high school group indicates a decrease in the stability of network centrality words. In terms of community-based trends, the modularity of high school group networks is similar to that of random networks, showing an upward trend in network modularity with increasing learning time. Centrality analysis shows that the coincidence rate of central words between the two networks reaches 66.67%, indicating good stability, but the centrality of other nodes fluctuates greatly.

4. Discussion

Research shows that as learning time increases, learners' second language vocabulary semantic networks exhibit a developmental trend at the macro, meso, and micro levels. The network scale expands, the small world features and modularity are enhanced, but the connection density decreases. The increase in modularity helps to maintain small world features, while the clarity of network partitioning reflects an increase in the number of word nodes and connections. The stability of central words plays a supporting role in network structure, but the dependence of the network on central nodes gradually decreases, and the influence of non central nodes increases. The development and changes of second language vocabulary semantic networks are mainly manifested in the selective connection of new nodes to existing nodes, resulting in enhanced network partitioning. In teaching, we should strengthen the teaching of central words, promote vocabulary association, use category mapping methods to optimize the semantic network structure of vocabulary, and improve vocabulary production ability.

5. Conclusion

This study reveals the development of the semantic network structure of Chinese middle school students' second language vocabulary, and finds that with the increase of learning time, the number of network nodes and connections increases, but the organization mode of psychological vocabulary needs further optimization. Research has shown that the small world and non-linear development characteristics of the semantic system of second language vocabulary have enlightening implications for teaching. It is recommended that teachers pay attention to the semantic connections between words and arrange the order of vocabulary learning reasonably. In addition, research on network structure has facilitated communication between psycholinguistics and vocabulary acquisition, and its methods can be applied to a wider range of semantic categories and language representation studies.