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The difficulties of offline education caused by the COVID-19 epidemic are attracting increasing public attention. Although international research on online education platforms is gradually deepening, the research on the influencing factors of Chinese users' participation in these platforms shows different results due to regional differences. Therefore, this study adopts the technology acceptance model (TAM) as the basis to build the index system of influencing factors for the Tencent conference online education platform. The questionnaire design covered five dimensions, including external environment, platform satisfaction, and continuous use intention. An online questionnaire survey was conducted on participants in some regions of China, 320 questionnaires were distributed, and 297 valid questionnaires were collected. The data were analyzed by SPSS 26.0, and the results of Cronbach's coefficient of 0.939 and a KMO value of 0.941 were obtained. The weight value, entropy value, and importance ranking of the factors were determined by combining hierarchical analysis and the entropy weighting method. First, the perceived ease of use is the most critical factor influencing the users' use of online delivery platforms. Second, freedom from geographical time difference, the ease of operation, system perfection, and proficiency in use greatly influenced the participants' use of the online lecture platform. Third, the study group suggested that the platform should be optimized in terms of convenience, stability, and freedom from geographical time difference, and provide reasonable references and lessons for future educational efforts. [ABSTRACT FROM AUTHOR]

China's forest ecological problems are becoming increasingly serious, especially in the Yangtze River Basin (YRB) area. The basin has rich species resources and a well-developed natural forest management and conservation policy. Taking the YRB as the object, we combine the DPSIRM model to build a forest evaluation system containing 6 criterion layers and 24 indicator layers. The entropy weight method-TOPSIS and ArcGIS were combined to assess the forest state and the distribution characteristics of the 11 regions. Furthermore, grey relational analysis (GRA) was used to study the influencing factors of forest status. The results are as follows: (1) the comprehensive index of the YRB forests increased by 192.66% during the study period. The forest status showed the stage characteristics of small climb, basic flatness, and significant improvement. (2) The forest status varied significantly among provinces (cities), with Tibet (0.483) in the best condition and Qinghai (0.103) in a worse condition. (3) Except for Tibet, the rest of the regions are more influenced by the extent of development of the economy. (4) The factor most strongly correlated with the YRB is the forest response (R) indicator. [ABSTRACT FROM AUTHOR]