Appendiceal neuroendocrine neoplasms (ANENs) account for a large proportion of all types of appendiceal malignancies, and one of the important diagnostic criteria is the size of Ki-67 proliferation index. The calculation of the Ki-67 index mainly depends on the experience of pathologists; however, the manual counting process is tedious. Therefore, this study aims to calculate the number of positive and negative cells in the ANEN pathological sections using computer image analysis algorithm and calculate the Ki-67 index to assist the pathologist for conducting a comprehensive evaluation. Aiming to resolve the over-segmentation problem of the traditional watershed algorithm, we used a forced minimum technique to modify the distance and propose an improved watershed algorithm. Then, we segmented, recognized, and counted the positive and negative cells in pathological images, and matched them with the pathologist’s gold standard. Our results show that the average accuracy of the proposed algorithm for the negative cells’ segmentation is 93.4%, while the average over-segmentation rate is 3.3%. Moreover, the average accuracy of the Ki-67 index and average error rate of computer-aided processing ANEN pathological images are 93.2% and 6.8%, respectively. The average time of processing ANENs pathological images decreases artificial time consumption from 57.4 s to 29.5 s, which improves the working efficiency.