Automated painting color matching technology based on semantic intelligence understanding

Abstract

Painting color matching technology is widely used in the production and printing process of products. Traditional painting and color matching have been unable to meet market demands. Based on this, a large-scale corpus under the existing semantic intelligent understanding system is used as the knowledge source. The computer automated painting color matching model is constructed. It is applied in case studies to address issues such as unclear query intentions, mismatched system retrieval terms, and return errors caused by uncertain factors such as synonyms and polysemy. This provides new ideas for the application of semantic intelligence understanding and automated painting color matching technology. The experimental results showed that the precision, recall, and F1 of the method used in the research were 0.8639, 0.8026, and 0.8309, respectively, significantly superior to commonly used methods. This indicates that the proposed automated painting color matching technology based on semantic intelligent understanding has high performance, which can effectively meet the painting color matching requirements.

Keywords

• Semantic intelligence understanding
• Automated painting color matching technology
• Anti neural network algorithm
• Support vector machine
• Word2vec