In astronomy, stars whose brightness changes with time are called variable stars. It is of great significance to study the distance of galaxies, the evolution of stars and the properties of stars in different stages. At present, the identification of variable stars mainly depends on observing their brightness changes for a long time and analyzing the spectrum of stars. This work requires astronomers to invest much time, so it is not easy to carry out large-scale classification. A data fusion method of photometric image and one-dimensional spectrum is proposed to classify variable stars. Experiments are carried out on identifying eclipsing variable stars, pulsar variable stars and standard stars. The Sloan Digital Sky Survey project includes photometric images and spectral data. For spectral data, this paper selects the flow value in the wavelength range of 380.0~680.0 nm. The photometric image comprises five data bands: u, g, r, i and z. The corresponding center wavelengths are 355.1, 468.6, 616.6, 748.0 and 893.2 nm respectively. The photometric value is generally distributed between 0 and 200. For the image part, this paper uses the data of five bands for classification, and locates the position of the target star in the photometric image through the star catalogue. In order to facilitate network training, photometric and spectral data are standardized. The method of combining photometric image and spectral data for classification proposed in this paper uses four indicators: accuracy, recall, F1 value and average accuracy. The experimental results after data fusion are better than those using spectral or photometric data alone. For the classification tasks of eclipsing binaries, pulsars and standard stars, the accuracy rates are 91.1%, 92.8% and 98.2% respectively. Experiments show that the combination of image data and photometric data is an effective method for star classification, which provides a new idea and method for star classification in the future.