Eye-movement interaction has a broad application prospect in the field of human-computer interaction. Aiming at the problems of traditional eye-movement interaction sensors, such as universal intrusiveness, complex calibration process and high price, low resolution of common monocular camera sensors, etc., an eye movement recognition method based on front-facing camera video using directional gradient histogram (HOG) features + SVM + LSTM neural network, and a simple human-computer interaction application are proposed in this study. Firstly, the region of eyes are localized and tracked after face alignment. Secondly, the open-close and non-blinking state of the eyes is judged by the SVM model. Then, the position of eye center between adjacent frames is analyzed to roughly judge the eye movements, and the suspicious interframe difference video sequence of intentional eye position is obtained, which is the input of the LSTM network for prediction, and then trigger computer commands to complete the interaction. Through the self-made data sample set (about 10% of which are negative samples), the accuracy of dynamic blink recognition is better than 95%, and the accuracy of eye movement behavior prediction is 99.3%.