Modeling and Optimization of Library Information Overload Cognitive Load Based on Multi-factor Interaction Model and CNN-LSTM Fusion Network
收藏Figshare2025-08-21 更新2026-04-28 收录
下载链接：
https://figshare.com/articles/dataset/_b_Modeling_and_b_b_O_b_b_ptimization_of_b_b_L_b_b_ibrary_b_b_I_b_b_nformation_b_b_O_b_b_verload_b_b_C_b_b_ognitive_b_b_L_b_b_oad_b_b_B_b_b_ased_on_b_b_M_b_b_ulti-factor_b_b_I_b_b_nteraction_b_b_M_b_b_odel_and_CNN-LSTM_b_b_F_b_b_usion_b_b_N/29957228
下载链接
链接失效反馈官方服务：
资源简介：
With the explosive growth of library information resources driven by big data and artificial intelligence technology, users are facing increasingly serious information overload, which significantly increases cognitive load and affects learning and mental health. Therefore, this paper integrates cognitive neuroscience and deep learning, constructs a multi-factor interaction model, incorporates variables such as information amount, task complexity, user background, and time pressure into a unified framework, and uses a CNN-LSTM hybrid network to perform real-time feature extraction and fusion of 256 Hz EEG signals and behavioral data to achieve high-precision prediction of cognitive load
创建时间：
2025-08-21
