Metaphorical Meaning Integration in Poetry Based on Online Discourse Data: Analysis from a Cognitive Linguistics Perspective

As an emerging literary form, online poetry has garnered significant attention due to its rapid dissemination, diverse styles, and complex metaphorical expressions. However, the process of metaphorical meaning integration in poetry is difficult to quantify, necessitating support from Artificial Intelligence technologies. This study integrates cognitive linguistics theory with AI algorithms to propose a three-dimensional fusion analysis framework—“cognitive theory + specific AI algorithms + online discourse data”—for dissecting metaphorical meaning integration in online poetry. By constructing a comprehensive methodology encompassing metaphor identification, semantic mapping, and integration analysis, this study offers a novel quantitative pathway for metaphor research in poetry. Experimental validation demonstrates that the integrated approach—leveraging Support Vector Machines (SVM), Convolutional Neural Networks (CNN), BERT pre-trained models, and the DeepSeek-R1 large model—achieves outstanding performance in metaphor recognition accuracy, semantic association quantification, and fusion effectiveness evaluation, fully embodying both theoretical and practical value.