The rapid progress of Internet technology has accelerated the development of natural language processing technology. To address the current issue of poor adaptability and accuracy in cross-language text matching and translation, firstly, a multihead attention mechanism and convolutional neural network are introduced. Moreover, a cross-language text matching model based on similarity-based attention convolutional neural network is constructed. Then, visual features are added to the Transformer model to build a real-time machine translation model based on the improved Transformer. The results showed that the accuracy of the proposed text matching model could reach 83.42% when the epoch was 4. The proposed model achieved accuracy rates of 78.96%, 77.55%, and 79.86% in the experiment of matching French, German, and Spanish with English, respectively, while the accuracy rates were 79.16%, 75.03%, and 76.54% in the experiment of matching English with three languages. In addition, as the training data size increased from 1 M to 3 M, the Bilingual Evaluation Understud score of the proposed translation model improved by 36.45%, demonstrating good scalability. In summary, the model constructed in the study not only has high accuracy and adaptability but also demonstrates significant advantages in scalability.