This paper completes the overall design of a linguistic interactive terminology database based on the characteristics of second language acquisition and terminology and completes the construction of the terminology database by combining a goodness-of-fit detection algorithm based on terminology eigenvalue extraction. The efficiency of terminology information recognition is analyzed and compared with the terminology conversion rate of the eigenvalue goodness-offit algorithm using a neural network learning model of long and short-term memory to optimize the performance of the terminology database. The metric approach's classifier performance evaluation metrics are used to compare the accuracy and recall of the two algorithms accurately. The results show that the accuracy of the fitted superiority classifier with the application of word eigenvalue embedding compared to the LSTM classifier for the classification of electric power terms is improved by about 11% in all categories, and the average accuracy of the classifier exceeds 76.5%.