Data and Information Center, Wuxi Vocational Institute of Commerce, Wuxi 214153, Jiangsu, China.
Comput Intell Neurosci. 2022 Aug 27;2022:2114114. doi: 10.1155/2022/2114114. eCollection 2022.
Currently, under the impact of the COVID-19, college students are facing increasingly elevated employment pressure and higher education pressure. This can easily cause a huge psychological burden on them, causing affective cognition problems such as anxiety and depression. In the long run, this is not conducive to students' physical and mental health, nor is it conducive to the healthy development of the school and even the whole society. Therefore, it is imperative to build a novel adaptive affective cognition analysis model for college students. In particular, in the context of smart cities and smart China, many universities have opened the smart campus mode, which provides a huge data resource for our research. Due to problems of the low real-time evaluation and single data source in traditional questionnaire evaluation methods, evaluation errors are prone to occur, which in turn interferes with subsequent treatment. Therefore, for the purpose of alleviating the above deficiencies and improving the efficiency and accuracy of the affective cognition analysis model of college students, this paper studies the adaptive affective cognition analysis method of college students on basis of deep learning. First, because students' psychological problems are often not sudden, on the contrary, most of these abnormalities will leave traces in their daily activities. Therefore, this paper constructs a multisource dataset with the access control data, network data, and learning data collected from the smart campus platform to describe the affective cognition status of students. Second, the multisource dataset is divided into two categories: image and text, and the CNN model is introduced to mine the psychological characteristics of college students, so as to provide a reference for the subsequent affective cognition state assessment. Finally, simulation tests are developed to confirm the viability of the technique suggested in this research. The experiments demonstrate that the accuracy of the assessment model is significantly increased because it can fully reflect the heterogeneity and comprehensiveness of the data. This also highlights that the new method has a wide range of potential applications in the modern campus setting and is also helpful in fostering the accuracy and depth of college students' work on their affective cognition.
目前，受新冠疫情影响，大学生面临着日益增大的就业压力和高等教育压力。这很容易给他们造成巨大的心理负担，引发焦虑、抑郁等情感认知问题。长此以往，不仅不利于学生身心健康，也不利于学校乃至整个社会的健康发展。因此，构建新型的大学生自适应情感认知分析模型势在必行。特别是在智慧城市和智慧中国的背景下，许多高校已经开启了智慧校园模式，这为我们的研究提供了巨大的数据资源。由于传统问卷调查评估方法存在实时性评价低、数据源单一等问题，评估容易出现误差，进而影响后续的处理。因此，为了缓解上述缺陷，提高大学生情感认知分析模型的效率和准确性，本文基于深度学习研究了大学生自适应情感认知分析方法。首先，由于学生的心理问题往往不是突发的，相反，这些异常大多会在他们的日常活动中留下痕迹。因此，本文构建了一个多源数据集，该数据集包含从智慧校园平台收集的访问控制数据、网络数据和学习数据，用于描述学生的情感认知状态。其次，将多源数据集分为图像和文本两类，并引入 CNN 模型挖掘大学生的心理特征，为后续的情感认知状态评估提供参考。最后，开发了模拟测试来验证本研究中提出的技术的可行性。实验表明，评估模型的准确率显著提高，因为它可以充分反映数据的异质性和全面性。这也突显了新方法在现代校园环境中具有广泛的潜在应用，有助于提高大学生情感认知工作的准确性和深度。
