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Dry matter accumulation (DM) and yield assessment are the key issues of maize production management and market of silage and deep processing. A three-year experiment (2017—2019) was carried out in maize fields of Changchun, Jilin Province, monitoring soil temperature (20cm and 40cm) in root zone, air temperature, canopy temperature and dry matter accumulation in maize growth stages. The Logistic model and the normalized Logistic model were established based on different effective accumulated temperatures with the observation in each year, separately. Then the validations of each normalized Logistic model were implemented by the observations in the others two years. Results showed that the Logistic models were varied in parameters with the data in different locations and years, based on the four effective accumulated temperatures (soil temperature at 20cm and 40cm, air temperature and crop canopy temperature) to simulate dry matter of single maize. However, the normalized Logistic model made a good performance in simulating maize dry matter accumulation under the normalized method. The statistical parameters of root mean square error, relative error, the coefficient of determination, and the index of model agreement reached good values between the simulation and observation. Especially for the normalized Logistic model in 2019, it was run better in the validation of 2017 and 2018. At the same time, the simulations effect was better in effective accumulated canopy temperature of normalized Logistic model. Thus it can be seen that the crop canopy temperature would be more available in regional irrigation management with its scale extension. These research results could support the precision irrigation system and management in irrigation district.