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Abstract. The special performance of heat storage and heat preservation of solar greenhouse in winter can realize out-of-season production. But the uneven canopy temperature distribution in greenhouse affects crop growth. This paper takes the solar greenhouse with short back slope and high thick wall (length 50m, span 7m) as the research object. Based on the 40-channel PT100 temperature field monitoring system, the all-weather dynamic monitoring of tomato canopy in winter was carried out. Based on Support Vector Regression and Differential Evolution (DE-SVR) algorithm, a method of fitting the canopy temperature field and optimizing the maximum characteristic points in solar greenhouse was proposed. Via this method, the characteristic points of canopy temperature distribution under different weather conditions were obtained. The results showed that the indoor canopy temperature was greatly affected by external light, and the temperature increased sharply after rolling the shutter. The change range of the solar greenhouse highest temperature in sunny and cloudy days was 10.4~37.1 ℃, 12.3~20 ℃, and the lowest temperature was 7.5-23.6 ℃, 8.9-16.2 ℃, respectively. The highest temperature in the greenhouse canopy appeared at 13: 00 at noon, and the lowest temperature appeared before and after sunrise, and the temperature decrease in sunny days was generally greater than that in cloudy days. The continuous temperature distribution of the canopy is obtained by fitting the temperature field data by spline interpolation and SVR algorithm. The fitting correlation coefficient R² is higher than 0.99. The optimization results show that under different weather conditions, the characteristic points of high temperature and low temperature are basically the same, and the high temperature mainly occurs in the greenhouse [22.4 m, 2.4 m]. The characteristic point of low temperature mainly appears near the outer membrane [4.1 m, 5.3m] in the east of greenhouse. The acquisition of feature points provides a theoretical basis for greenhouse cultivation and sensor deployment.