Habitat suitability mapping can provide information on spatial variation of wildlife habitat suitability which is useful for wildlife population management and conservation planning. The key issue of habitat suitability mapping is how to construct habitat suitability model (HSM). Existing presence-only based methods of constructing HSM cannot explicitly express the quantitative relationship between wildlife habitat suitability and environmental factors. So the resulted HSM would be insufficient to express the ecological effects of environmental factors on wildlife habitat use. In this paper we proposed a kernel density estimation-based method of using wildlife occurrence data to construct HSM for wildlife habitat suitability mapping, which can obtain the quantitative ecological relationship between habitat suitability and environmental factors. Under the assumption that habitat should be more suitable where wildlife presented more frequently, the presence probability density function (PDF) estimated from wildlife occurrence data was used to express the quantitative ecological relationship between habitat suitability and each individual environmental factor. Then, assuming that habitat suitability is determined by the limiting factor, a minimum operation was applied to construct the final HSM by synthesizing all these relationships on an environmental factor set. In the case study we applied the proposed kernel density estimation-based method to habitat suitability mapping of the White-tailed deer (Odocoileus virginianus) in Voyageures National Park based on 365 deer occurrence points from aerial survey and environmental data characterizing its living environment, with the aid of geographic information system techniques. The environmental factors selected were snow depth, land cover type, forest edge length, and slope gradient. Cross validation was used to evaluate the performance of the proposed method. Results showed that the mean of continuous Boyce Indexes calculated from 10 repetitions of 2-fold cross validation was 0.75 with a standard deviation of 0.11. This suggests that the HSM from the proposed method has a good predictive capability. The proposed kernel density estimation-based method well captures the ecological effects of environmental factors on the wildlife habitat use in the form of quantitative relationship between wildlife habitat suitability and environmental factors. From the perspective of model interpretability, the proposed method is better than other HSM-constructing methods.