Research on the Application of Artificial Intelligence in Computer Network Technology in the Era of Big Data
Abstract
With the steady development of society, the development effect of computer network technology has been extremely good, and its prospects for development are also relatively broad. The integration of computer network technology into the production and development process of modern society has already had an impact on people's daily lives, providing convenient conditions for their clothing, food, housing, and transportation. With the emergence of modern big data and artificial intelligence technology, the direction for the intelligence of computer network technology has been pointed out. Applying this modern technology to computer network technology can better improve the overall level of computer technology and provide high-quality services for the development of society. This article briefly analyzes the relevant concepts of big data era and artificial intelligence, introduces the advantages and necessity of applying artificial intelligence in computer network technology in the big data era, and elaborates on the practical application of artificial intelligence in computer network technology in the big data era.
References
Jin, X., Yang, Z., Lin, X., Yang, S., Qin, L., & Peng, Y. (2021, April). Fast: Fpga-based subgraph matching on massive graphs. In 2021 IEEE 37th international conference on data engineering (ICDE) (pp. 1452-1463). IEEE.
Lyu, T., Gu, D., Chen, P., Jiang, Y., Zhang, Z., Pang, H., ... & Dong, Y. (2024). Optimized CNNs for Rapid 3D Point Cloud Object Recognition. arXiv preprint arXiv:2412.02855.
Luo, Y., Wei, Z., Xu, G., Li, Z., Xie, Y., & Yin, Y. (2024). Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization. Journal of Theory and Practice of Engineering Science, 4(02), 52–57. https://doi.org/10.53469/jtpes.2024.04(02).08
Huang, S., Liang, Y., Shen, F., & Gao, F. (2024, July). Research on Federated Learning's Contribution to Trustworthy and Responsible Artificial Intelligence. In Proceedings of the 2024 3rd International Symposium on Robotics, Artificial Intelligence and Information Engineering (pp. 125-129).
Huang, S., Diao, S., Wan, Y., & Song, C. (2024, August). Research on multi-agency collaboration medical images analysis and classification system based on federated learning. In Proceedings of the 2024 International Conference on Biomedicine and Intelligent Technology (pp. 40-44).
Ukey, N., Zhang, G., Yang, Z., Li, B., Li, W., & Zhang, W. (2023). Efficient continuous kNN join over dynamic high-dimensional data. World Wide Web, 26(6), 3759-3794.
Peng, Q., Planche, B., Gao, Z., Zheng, M., Choudhuri, A., Chen, T., ... & Wu, Z. (2024). 3d vision-language gaussian splatting. arXiv preprint arXiv:2410.07577.
Zhou, Y., Wang, Z., Zheng, S., Zhou, L., Dai, L., Luo, H., ... & Sui, M. (2024). Optimization of automated garbage recognition model based on resnet-50 and weakly supervised cnn for sustainable urban development. Alexandria Engineering Journal, 108, 415-427.
Fan, Y., Wang, Y., Liu, L., Tang, X., Sun, N., & Yu, Z. (2025). Research on the Online Update Method for Retrieval-Augmented Generation (RAG) Model with Incremental Learning. arXiv preprint arXiv:2501.07063.
Tian, Q., Wang, Z., & Cui, X. (2024). Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism. arXiv preprint arXiv:2409.13626.
Xu, Y., Shan, X., Lin, Y. S., & Wang, J. (2025). AI-Enhanced Tools for Cross-Cultural Game Design: Supporting Online Character Conceptualization and Collaborative Sketching. In International Conference on Human-Computer Interaction (pp. 429-446). Springer, Cham.
