Peripheral neuroblastic tumors (pNT) are common extracranial malignant solid tumors in children, and its main prognostic evaluation is based on differentiation degree of neuroblastic tumor and mitosis-karyorrhexis index (MKI). At present, the calculation of MKI is mainly done manually by pathologists, which is a cumbersome process with a large workload. The computer image processing algorithm is used to identify pathological mitotic neuroblasts (PMN) and neuroblasts (NEU) in pathological slice images, and assist pathologists in counting, which can reduce doctors' repetitive work and improve doctors' work effectiveness. The mathematical morphology local minimum mark (H-minima) is used to modify the gradient amplitude, and the improved watershed algorithm is used to identify and count NEU. The experimental results show that, compared with the gold standard of pathologists, the average accuracy rate of the proposed algorithm for NEU recognition is 94.2%, and the average over-segmentation rate is 2.79%. From the perspective of chromaticity components, the average recognition accuracy of PMN cytoplasmic regions is 81.66%, and the average error rate of MKI value is 0.031%.