Development of an Interactive English Vocabulary Learning Mobile Application Based on Scene Image Recognition

This study presents an intelligent framework for English writing generation that integrates contextual semantics with cognitive modeling to improve writing quality and personalization. The proposed system introduces the Contextualized Semantic Composition Network. This network combines syntactic structure modeling with semantic coherence within a hierarchical attention architecture. Through syntax-aware encoding and semantic-context fusion, the model captures long range linguistic dependencies and maintains alignment between grammatical structure and thematic intent. As a result, local expressions remain consistent with global discourse, enabling the generation of coherent and stylistically appropriate text. Based on this framework, a Hierarchical Cognitive Alignment Mechanism is introduced to simulate cognitive processes involved in human writing. This mechanism includes multi-level cognitive tracking, adaptive cognitive modulation, and global consistency control. These components model the interaction among syntax, semantics, and pragmatic intent during text generation. The mechanism ensures smooth transitions between cognitive states and maintains thematic stability, allowing the generated text to achieve both grammatical correctness and natural reasoning. Experimental results on multiple benchmark datasets demonstrate that the proposed framework outperforms existing models in accuracy, fluency, and contextual coherence. The integration of structural modeling and cognitive alignment enhances both adaptability and consistency. These improvements contribute to more effective and personalized English writing support, and provide insights into the integration of linguistic theory, cognitive modeling, and artificial intelligence in educational applications.