Use Computer Vision and Natural Language Processing to Optimize Advertising and User Behavior Analysis

Abstract

This paper focuses on the technical application of computer vision and natural language processing in automatic advertising placement and user behavior analysis, emphasizing the integration of visual and textual data as the foundation of intelligent decision-making. Building upon the principles of image recognition, semantic understanding, and multimodal content alignment, the study explains how deep learning models extract visual attributes, identify objects and scenes, and interpret text semantics to support precise advertising content generation. By introducing multimodal learning mechanisms, the system can jointly analyze visual cues and linguistic information, thereby enabling richer meaning construction and more accurate situational matching for advertising materials. In this work, an intelligent automatic optimization pipeline is established, covering integrated content generation, personalized customization of advertising materials, dynamic scheduling, and real-time delivery. The framework supports automated creative generation, adaptive content adjustment based on user context, and continuous performance feedback to improve placement strategies. Furthermore, a refined user behavior model is developed by analyzing users' visual interactions-such as gaze patterns, browsing duration, and click distributions-as well as their textual behaviors including search queries, comments, and linguistic preferences. These behavioral signals are embedded into user feature representations to predict potential actions, identify latent interests, and enhance audience segmentation. Overall, the proposed multimodal intelligent advertising system significantly improves ad-matching precision, strengthens behavioral prediction capability, and increases the final conversion rate. The study provides valuable technical references for next-generation intelligent advertising platforms that seek to integrate multimodal perception, adaptive optimization, and user-centric behavioral analytics.