Development and Model Research of English Vocabulary Learning System Based on Deep Learning

This manuscript introduces an English lexicon acquisition platform propelled by advanced deep learning methodologies, designed to bolster the proficiency of English vocabulary among learners through sophisticated computational techniques. At the heart of this system lies the application of deep learning protocols to distill the intrinsic characteristics of English lexicon, encompassing denotations, applications, and contextual usage. Utilizing natural language processing capabilities, the framework is adept at interpreting and emulating the natural human linguistic acquisition trajectory, offering a bespoke educational journey for each learner. During the phase of algorithmic conception, the system harnesses recurrent neural networks (RNNs) along with long short-term memory networks (LSTMs) to parse lexical series, thereby identifying inter-word relationships. Moreover, convolutional neural networks (CNNs) are deployed for the extraction of features, augmenting the model's capacity to represent complex patterns. Systematic simulation trials were conducted utilizing an expansive textual database. Upon assessment across a spectrum of 1000 learners with varying degrees of English proficiency, the outcomes indicate a marked enhancement in vocabulary acquisition, with an average expansion rate surpassing 30%, and an increase in learning efficacy of 25% relative to conventional pedagogical approaches. Data analytics confirm the system's superior performance in the realms of vocabulary retention, comprehension, and practical application, presenting a novel avenue for efficient and intelligent English lexicon acquisition.