Research on Intelligent Language Translation Algorithm Based on Computer Vision

The rapid development of the Internet has led to an exponential growth of multilingual information contained in the network, and traditional translation is difficult to meet the needs of users, and intelligent language translation has important research value and application prospects. This study adopts convolutional neural network to extract the visual features of translated images, and analyzes the correlation between image and text features by using the mechanism of region-selective attention to align the features of text and image in the translated information. Then the two information features are fused and processed, and input into the sequence model to realize the intelligent language translation, so as to obtain the intelligent language translation algorithm based on computer vision. The research results show that the intelligent language translation algorithm in this paper has comprehensive advantages in several key evaluation indexes, highlighting its performance in improving the quality of translation language generation. The application in translation real-world scenarios is able to maintain a low leakage (1.30%) and mistranslation rate (2.64%), and the translation response time is also able to be maintained at around 67.28ms. The proposed intelligent language translation algorithm has high advantages in performance test and application in real scenarios, has good generalization and applicability in multilingual translation, and is expected to be more widely used in the future.