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The novel coronavirus disease which originated has been medical as Covid 19 by the World Health Organization (WHO). The early onset in December 2019. It revealed that. A virus has been the causative factor for this pandemic. A phylogenetic research has been carried out with achievable based on data analytic model. In this situation, individuals with this disease have improved more features produce poor classification accuracy. To resolve this problem, we propose an adaptive social spider featured decisive Convolution Neural network for Covid-19 deficiency infection based on risk prediction to enhance the prediction and evaluation of infectious diseases. Both GIS and the Covid data sets are used in the process. The Social spider Feature Selection (SSFS) selects features depending on Disease deficiency infection rate (DDIR) and Spreading habitual factor (SHF). By selecting these features decisive Convolutional Neural Classifier (DCNN) trains the value to trained set to classify the risk based on the feature predicting weights. This proposed system produce higher prediction rate compared to the other methods as well in precision and recall rate.