Due to observations of environmental impact carried out by monitoring stations on the China Mainland, we need to deal with the daily data for many years for daily tracking purposes. Existing filtering methods include the wavelet method, least-squares co-location method, Gaussian weighted average method, and Vondark method, etc., but for daily tracking these methods prove to be inconvenient. Aimed at solving the problem of extracting efficient information from sequential deformation observation data over some years, a filtering method, based on multi-kernel function, is studied in this paper. Taking continuous GPS station vertical sequential observation data as an example, we discuss the parameters for the multi-kernel function and their physical meaning. Conclusions are as follows:(1) When the kernel function index is 0.5 and the smoothing factor 0.003, the mean square error of unit weight of the filtering model with a kernel point interval of more than 10 days, is the least. (2) The kernel point interval controls the level of the filtering information frequency spectrum, the larger the interval, the lower the spectrum information; the smaller the interval, the higher the spectrum information. (3) Sometimes kernel points are lost because of missing data. When more than two continuous points are lost, the filtering fails; when two continuous points are lost, part of the filtering waves are distorted because of the missing data; when just one point is lost, the filtering effect is not affected. (4) From the filtering application in the GPS time-series data, the fixed-point deformation time-series data, and the non-tectonic deformation time-series data, information on different spectra are obtained and the stability and reliability of the method verified. This provides a more convenient way to daily process time-series observation data from a large number of stations.