With the deep integration of information technology and Internet of Things (IoT) technology in the field of higher education, asset management in universities faces severe challenges posed by diverse asset types, widespread distribution, and dynamic changes. Traditional manual inventory methods and partial automation solutions are increasingly unable to meet the management demands of modern smart campuses in terms of efficiency, accuracy, and cost. This paper proposes and implements an automatic asset auditing and management method based on the Splunk big data analytics platform, specifically tailored for university scenarios. This method constructs a real-time digital profile of assets by uniformly collecting multi-source heterogeneous data generated by network devices, servers, classroom terminals, audio-visual systems, and various IoT devices. At the methodological level, this paper designs an automatic asset status confirmation framework based on identifier correlation, a physical location anomaly detection mechanism derived from network topology, and cross-validation rules for data integrity. Experimental results in a university environment demonstrate that this method can increase the automation coverage of asset inventory to over 95%, reduce auditing time by 92%, and achieve an accuracy rate of up to 99%. This not only significantly lowers labor costs and operational complexity but also enhances the security and compliance of teaching and research assets through real-time anomaly alerts, providing an efficient and reliable intelligent solution for large-scale, dynamic university asset management.