Dynamic feature evaluation and concept evaluation is major challenging task in the field of data classification. The continuity of data induced a new feature during classification process, but the classification process is predefined task for assigning data into class. Data comes into multiple feature sub-set format into infinite length. The infinite length not decided the how many class are assigned. Support vector machine is well recognized method for data classification. For the process of support vector machine evaluation of new feature during classification is major problem. The problem of feature evaluation decreases the performance of Support Vector Machine (SVM). For the improvement of support vector machine, particle of swarm optimization technique is used. Particle of swarm optimization controls the dynamic feature evaluation process and decreases the possibility of confusion in selection of class and increase the classification ratio of support vector machine. Particle of swarm optimization work in two phases one used as dynamic population selection and another are used for optimization process of evolved new feature.