Herein, a method to represent a mitosis event is proposed by using the feature of cell evolution in the time domain. First, three kinds of features are extracted for each frame of the mitotic sequence, i.e., the generalized search tree, scale invariant feature transformation, and convolutional neural network. Each series of extracted features is handled using the pooling method in spatial and temporal dimensions. Subsequently, the processed series of pooling features are combined into a vector to represent the final mitotic event characteristics. Finally, the combined feature vector is used as the classifier input, and the traditional machine learning method of support vector machine is used to address the mitotic recognition problem. The experimental results denote that the proposed method is superior to the traditional method with respect to the precision and recall rate and is more appropriate for mitosis detection applications.