With the gradual improvement of industrial automation and informatization, traditional measurement and control systems are facing performance limitations and functional deficiencies, and urgently need to be upgraded by advanced computer technologies to meet the demands of modern mass production. This study adopts a variety of computer technologies, including artificial intelligence algorithms, data analysis and cloud computing technology, to intelligently transform and optimize the traditional measurement and control system. Specific methods include the use of machine learning algorithms to analyze and process the collected data to improve the accuracy and processing speed of the data; the use of cloud computing platforms to achieve efficient storage and access to the data; and the use of an intelligent decision support system to optimize the operational process and improve the response speed of the system. The study shows that the integration and application of these technologies not only significantly improves the operational efficiency and stability of the measurement and control system, but also enhances the intelligence level of the system to better adapt to the complex and changing operating environment and demand, which has an important engineering application value and broad market prospects. In the initial stage of system operation (0 to 24 h), the response time, accuracy and stability are maintained in a relatively stable state, and the resource occupancy rate gradually increases but not by much. Through an in-depth discussion of the application of computer technology in the upgrading of measurement and control systems, this study not only helps to understand the specific effects of the application of these technologies, but also provides valuable references and practical experience for the researchers in related fields.