Objective To explore the fuzzy evaluation based on Matlab and its application in the quantitative risk evaluation of tick?borne diseases. Methods The coniferous forest, mixed coniferous?broadleaf forest, and meadow in the Weizigou forest farm, Dongjingcheng town, Heilongjiang province, China were selected as three habitats for investigation. All ticks were manually collected with white cloth flagging. A thermohygrometer was used to record the temperature and humidity. The fuzzy evaluation indicators of tick?borne diseases were collected, and the fuzzy inference system was created to assess the indicators. Results From May to July 2012, the analysis of survey data showed that the risk score exhibited an overall downward trend, with a maximum value of 60.0, which indicated a relatively high level of risk, and a minimum value of 10.3, which indicated a low level of risk. In May 2013, the analysis of survey data showed that the risk score was 85.5 for all the three habitats, which indicated a high level of risk. According to 13 times of investigation, 46.16% of the habitats had a low level of risk, 15.38% had a middle level of risk, 7.69% had a relatively high level of risk, and 30.77% had a high level of risk. The overall risk of Weizigou forest farm is at a low level. Conclusion Temperature and humidity are important influential factors for the activity of ticks. It is of certain significance to select temperature, humidity, and tick density as indicators in the fuzzy evaluation of tick?borne diseases. Based on the fuzzy mathematical theory, the fuzzy evaluation has a rigorous theoretical basis and is scientific and rational in the risk evaluation of tick?borne diseases.