Ancient porcelain is a remnant of history and has non-regenerability, so the ideal ancient ceramic analysis technique should be nondestructive. In order to objectively and effectively identify ancient ceramics kiln, a non-destructive method has been developed based on ultraviolet, visible and near-infrared diffuse reflectance spectroscopy to identify ancient ceramic kiln objectively and effectively. In view of the lack of description of the target characteristics in the traditional single-band ancient ceramic kilns, for example, the diffuse reflectance spectroscopy data can reflect the color characteristics of ancient ceramics in the visible region, but the ceramics fired in the same kiln will have different color property, only based on the diffuse reflectance of the visible light band to identify the source of the kiln unreasonable, in the ultraviolet and near-infrared and ultraviolet light band, the ancient ceramic interior molecules and the band light after the diffuse reflectance spectral data reflect the ancient rich sample structure and material properties carried by the ceramic can effectively improve the expression of the features by combining the UV and near-infrared spectral reflectance spectroscopy data. Therefore, we propose a multi-band feature extraction method using ultraviolet, visible and near infrared. During the actual experiment, the average identification accuracy based on multi-band linear feature fusion kiln is 92.9%, which is 1.8% higher than the average accuracy of 91.1% for single-band kiln identification. The experimental results verify that the multi-band method is effective; In the process of feature extraction, wavelet transform is often used to process the spectral signal. However, since the ancient ceramic reflection spectrum wave signal is in the ultraviolet region, the waveform of the diffuse reflection spectrum in the visible and near-infrared region is not only fluctuating but also changing greatly in frequency. Therefore, it is very difficult to select the wavelet basis. In this paper, The feature extraction method is characterized by adaptively distributing the intrinsic mode functions of different frequency wavelets, selecting the appropriate intrinsic mode functions to extract the spectral characteristics of different wavelength bands of the ancient ceramics, but there exists an over-decomposition phenomenon in the decomposition process, that is to say, the intrinsic mode function of the false component. The average correlation coefficient and the mean contribution rate of all the intrinsic mode functions of all the samples and the decomposition are taken as the criteria for selecting the intrinsic mode function. The experimental results show that with the decomposition order increases, the average correlation coefficient and the mean variance contribution rate decrease, and when the decomposition order is 4, the contribution rate of correlation coefficient and variance are 0.30, but when the decomposition order is 5, the contribution rate of correlation coefficient and variance is only 0.15 and 0.18. Therefore, the fourth-order decomposition is chosen for feature extraction in different bands. On this basis, calculate the distribution matrix of different spectral features, use intra-class and class scatter matrix traces, calculate the weight of different band features when the feature is fused, the greater the weight, indicating that the greater the contribution of such features to the identification; Finally, the k nearest neighbor classifier is used to classify the ancient ceramics from different kilns. By comparing objectively and quantitatively the proposed method with similar methods, Zhu Xufeng used non-linear feature fusion method, and the average identification accuracy of kiln is 86.97%, and the method of this paper is 7.53% higher than this method. Liu Feng used covariance matrix to solve the feature weight of multi-band method, and the average identification accuracy of kiln is 89.63%, and the method of this paper is 4.87% higher than this method. The experimental results show that the proposed method is effective and feasible. It can be used as an effective auxiliary appraisal method for ancient ceramic kiln identification.