A Smart Campus Analysis Model Based on Multi source Data Fusion and Deep Learning
In order to solve the problems of data dispersion and information isolation in traditional campus management,this paper proposes a smart campus analysis model based on multi-source data fusion and deep learning.By utilizing multi-source data fusion technology and integrating data from different sources,more comprehensive and accurate cam-pus information is provided through IoT devices,sensors,social media,and other aspects.Through deep learning tech-nology,comprehensive analysis of school operations,student learning,teaching effectiveness,and other aspects is a-chieved.The core characteristics of the model include data fusion,application of deep learning models,and support for intelligent decision-making at all levels of the school.By utilizing the ResNet(Residual Network)model in Convolu-tional Neural Networks(CNN)and integrating data from multiple systems such as student information management,li-brary management,and facial recognition,a comprehensive analysis was conducted.Residual learning was introduced to solve the problems of vanishing and exploding gradients in deep network training through skip connections.This model aims to improve campus management efficiency,optimize resource allocation,and enhance the quality of teaching and student services.The paper provides a detailed introduction to the application of data fusion,deep learning algorithms,and the design of a smart campus analysis model.Its effectiveness has been verified through practical case analysis,with an accuracy rate of 95%.
Smart campusUniversity managementDeep learning algorithmsMulti source data fusionManagement efficiency
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