Wheat is one of the main raw materials for making steamed bread, and the water, protein and starch in wheat vary depending on the place of production and the degree of drying, which in turn affects the quality of processed steamed bread. Therefore, it is particularly important to quickly identify the place of origin and degree of drying of wheat. Sensory evaluation is a common method used to identify the origin and degree of drying in wheat, in contrast to sensory evaluation, spectral analysis techniques can identify information such as molecular structure in a sample. Based on this, this paper attempts to use the near-infrared and mid-infrared spectral fusion technology to achieve the simultaneous identification of wheat from different producing areas and different degrees of drying. In this study, wheat from two different origins were selected and microwave drying was used to pretreat the wheat from the two different origin so that the moisture content of the dried wheat decreased to 12%±0.5%, while the moisture content of the undried wheat was 18%±0.5%. They were marked as undried wheat A, dried A, undried wheat B, dried B and then ground into powder, sieved with a 100 mesh screen and placed in a sealed bag for use. Subsequently, the near infrared and mid-infrared spectral information of four wheat samples were collected, and then the raw spectral data collected were pre-processed using the standard normal variate transformation (SNVT) using Matlab 7.10 version. The principal component analysis was used to reduce the dimension of the preprocessed data, and then the linear and short-infrared (NIR) and mid-infrared (MIR) spectral data were identified using linear discriminant analysis (LDA) and support vector machine (SVM), respectively to create a recognition model. In addition, using the synergy interval partial least square (SiPLS) method, the characteristic spectral ranges of the near-infrared and mid-infrared spectral data of the wheat pretreated with the standard normal variable transformation (SNVT) were screened out. After the fusion of the characteristic spectral ranges of the near-infrared and mid-infrared spectral data, a linear discriminant analysis (LDA) and support vector machine (SVM) were used to establish the identification model of the fusion spectral information of wheat. The recognition rate of wheat identification model established by linear discriminant analysis (LDA) and support vector machine (SVM) under the same spectral data were compared and the near-infrared and mid-infrared spectral data of the same modeling method established the wheat identification model. Recognition rate, comparison of spectral data fusion under the same modeling method and single spectral data were used to establish the recognition rate of the wheat identification model. The results showed that using the same kind of spectral analysis method, the recognition rates of the four wheat identification models established using SVM were higher than those of wheat identification models established using LDA. The recognition rate of wheat identification model established by near-infrared spectral data using the same modeling method was better than that of wheat identification model established by mid-infrared spectral data. Under the same modeling method, the identification rate of the wheat identification model established by the fusion of the characteristic spectral interval data of the near-infrared and mid-infrared spectral data filtered by SiPLS was the highest. After the fusion of spectral data, the wheat identification model established with LDA was integrated. The recognition rate of the correction set was 98.75%, and the recognition rate of the prediction set was 97.50%. The recognition rate of the correction set and the prediction set of the wheat identification model established by combining this selected variable with the SVM reached 100.0%. Comparing the recognition rate of wheat identification model established by using single spectral data, the recognition rate of wheat identification model established after fusion of spectral data was significantly improved. This study compared the wheat model’s recognition rate established by spectral data from both vertical and horizontal directions. The results can provide a reference for the more accurate use of spectral fusion technology to establish wheat production areas and drying degree identification model.