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Theoretical research results such as computer big data analysis and machine learning are essential support for the design of convenient and effective deep learning models, however, existing studies seldom viewed this problem from the perspectives of computer big data sampling, parallel processing optimization, data preprocessing, and predictive maintenance. To fill in this gap, this paper researched the computer big data analysis and predictive maintenance based on deep learning. At first, the paper elaborated on the self-adaptively adjusted sampling and parallel processing optimization of computer big data, and gave the flow of computer big data preprocessing based on a deep learning model; then, it introduced the computer big data analysis and predictive maintenance method based on deep learning; at last, experiments were conducted to compare the performance of different Convolutional Neural Network (CNN) models and the results proved the effectiveness of the proposed model.

Theoretical research results such as computer big data analysis and machine learning are essential support for the design of convenient and effective deep learning models, however, existing studies seldom viewed this problem from the perspectives of computer big data sampling, parallel processing optimization, data preprocessing, and predictive maintenance. To fill in this gap, this paper researched the computer big data analysis and predictive maintenance based on deep learning. At first, the paper elaborated on the self-adaptively adjusted sampling and parallel processing optimization of computer big data, and gave the flow of computer big data preprocessing based on a deep learning model; then, it introduced the computer big data analysis and predictive maintenance method based on deep learning; at last, experiments were conducted to compare the performance of different Convolutional Neural Network (CNN) models and the results proved the effectiveness of the proposed model.

To contain the spread of Covid-19, the Ministry of Education of China postponed the opening of schools for face-to-face lessons and launched an initiative of "postponement of school without suspension of learning”. Therefore, nearly all educational institutions across China moved from traditional teaching practices to online teaching in the Spring semester of 2020. English language teachers in Chinese universities had to move their traditional physical instruction to online. The paper is designed to develop a conceptual framework of English as a Foreign Language (EFL) teachers’ acceptance of online teaching during Covid-19 in Mainland China. Based on the integration of the Technology Acceptance Model (TAM) and Ely's Eight Conditions of Change (ECC) and previous research, six constructs — attitudes, subjective norm, perceived ease of use, perceived usefulness, self-efficacy, and eight facilitating conditions are selected in the proposed framework. A comprehensive understanding of the framework is expected to provide a better insight of the factors that affect EFL teachers' acceptance of online teaching during the pandemic for the stakeholders and future studies.

To contain the spread of Covid-19, the Ministry of Education of China postponed the opening of schools for face-to-face lessons and launched an initiative of "postponement of school without suspension of learning”. Therefore, nearly all educational institutions across China moved from traditional teaching practices to online teaching in the Spring semester of 2020. English language teachers in Chinese universities had to move their traditional physical instruction to online. The paper is designed to develop a conceptual framework of English as a Foreign Language (EFL) teachers’ acceptance of online teaching during Covid-19 in Mainland China. Based on the integration of the Technology Acceptance Model (TAM) and Ely's Eight Conditions of Change (ECC) and previous research, six constructs — attitudes, subjective norm, perceived ease of use, perceived usefulness, self-efficacy, and eight facilitating conditions are selected in the proposed framework. A comprehensive understanding of the framework is expected to provide a better insight of the factors that affect EFL teachers' acceptance of online teaching during the pandemic for the stakeholders and future studies.