Cardiotocography (CTG) is a commonly used technique of electronic fetal monitoring (EFM) for evaluating fetal well-being, which has the disadvantage of lower diagnostic rate caused by subjective factors. To reduce the rate of misdiagnosis and assist obstetricians in making accurate medical decisions, this paper proposed an intelligent assessment approach for analyzing fetal state based on fetal heart rate (FHR) signals. First, the FHR signals from the public database of the Czech Technical University-University Hospital in Brno (CTU-UHB) was preprocessed, and the comprehensive features were extracted. Then the optimal feature subset based on the k-nearest neighbor (KNN) genetic algorithm (GA) was selected. At last the classification using least square support vector machine (LS-SVM) was executed. The experimental results showed that the classification of fetal state achieved better performance using the proposed method in this paper: the accuracy is 91%, sensitivity is 89%, specificity is 94%, quality index is 92%, and area under the receiver operating characteristic curve is 92%, which can assist clinicians in assessing fetal state effectively.