Considering the problem of traditional cervical cancer detection method that brings high false negative rate (FNR) and high false positive rate (FPR),a new abnormal cervical cells detection method of multi-spectral Pap smear is proposed in this thesis,on the basis of multi-spectral microscopic imaging technology and computer automotive recognition technology. At first,image in a specific wave band is segmented according to the relationship between intensity and spectrum of each pixel. Then,multi-spectral features of each pixel are extracted making use of improved cosine correlation analysis (CCA) algorithm. Combined with the characteristic of each cell’s area,final definition is made. Experiments have proved the new approach could identify abnormal cells efficiently as well as lower FNR and FPR. Considering the problem of traditional cervical cancer detection method that brings high false negative rate (FNR) and high false positive rate (FPR), a new abnormal cervical cells detection method of multi-spectral Pap smear is proposed in thesis, on the basis of Multi-spectral microscopic imaging technology and computer automotive recognition technology. At first, image in a specific wave band is segmented according to the relationship between intensity and spectrum of each pixel. Then, multi-spectral features of each pixel are extracted making use of improved cosine correlation analysis (CCA) algorithm. Combined with the characteristic of each cell’s area, final definition is made. Experiments have proven the new approach could identify abnormal cells efficiently as well as lower FNR and FPR.