The invention relates to the technical field of program tracking, in particular to a cross-platform television program
tracking system, which comprises an equipment prediction module for collecting
equipment use data of a user, constructing a
Bayesian network prediction model, analyzing a switching mode of the user among various equipment, and combining a watching progress and a
content type of a television program, and generating an equipment switching prediction result through
statistical analysis and prediction of the time for the user to replace the equipment. According to the method and the device, the switching mode of the user among various devices is analyzed and combined with the watching progress and the
content type of the television program, so that the user experience is optimized, and the seamless switching capability of program watching is enhanced. Besides, environmental factors such as position information, equipment electric quantity and network state are collected and analyzed in real time, so that the
system can adjust the prediction model according to the key environmental factors, and more accurate video cache adjustment is realized in evaluation of network speed and current buffer size.