The spectral data measured from spectral measurements are easily affected by human,environmental,equipment and other factors leading to the abnormal spectral characteristics and impacting analyses especially in spectral measurements in the fields.According to the situations of traditional spectral data in preprocessing and analysis,a novel algorithm used for abnormal data excluding adaptively and spectral data classifying automatically based on artificial intelligence was established.The Mahalanobis distance threshold by genetic algorithm searching was determined to exclude abnormal spectral data adaptively and to quantify the effect of excluding abnormal spectral consistency index (ACI).With the self-organizing neural network,spectral characteristics of various types of observing objects were used as input and classified automatically after removing the abnormal.The results showed that the algorithm achieved good excluding (ACI more than 86%) and classification (overall classification accuracy of 94%).It can be used to well automate the handling of excluding spectrum and spectral classification.