Informationization Design of College English Corpus Using Deep Learning Algorithms

This study proposes the ISSO-SNN model for semantic readability assessment in a college English corpus, addressing limitations of traditional methods. Using NLP preprocessing and TF-IDF features, the model leverages a Siamese network optimized with ISSO for improved prediction. The study proposes an Intelligent Shuffled Shepherd Optimized Siamese Neural Network (ISSO-SNN) model for semantic readability assessment in a college English corpus. Implemented in Python 3.11, it achieved high performance (accuracy 0.98, precision 0.97, recall 0.97 , F1 0.98 ), supporting effective and personalized English learning.