Option pricing has become an important part of the financial market.Because the market is dynamic,it is very difficult to accurately predict the option price.Therefore,various machine learning techniques are designed and developed to deal with the problem of predicting the future trend of option price.This paper compares the effectiveness of support vector machine(SVM) model and artificial neural network(ANN) model in option price prediction.In the testing and training phase,both models are tested with the publicly available benchmark data set,spy option price-2015.Both models use the data converted by principal component analysis(PCA) to achieve better prediction accuracy.On the other hand,in order to avoid the over fitting problem,the whole data set is divided into two groups:training set(70%) and test set(30%).The results of support vector machine model and neural network model based on root mean square error(RMSE) are compared.The experimental results show that the neural network model is better than the support vector machine model in that its predicted option price is in good agreement with the corresponding actual option price.