Automatically anomaly recognition in surveillance videos is a crucial issue for social security. A 3D-LCRN visual time series model was proposed for abnormal behavior recognition on video surveillance. Firstly, a structural similarity background modeling method was proposed to obtain corrected optical flow and corrected motion history image, which was insensitive to illumination variation and background moving against background interference in complex scenes. Secondly, a new sample expansion method was proposed to solve the imbalance between normal training samples and abnormal ones, which enriched the spatial and temporal information of samples from both dimensionality and quantity. On dimensionality, the method stacked corrected optical flow and corrected motion history image to generate the corrected optical flow motion history image. In quantity, COFMHI was randomly cropped and clustered into center visual words by K-means. Finally, COFMHI was used as 3D-CNN input to extract local short-time spatial-temporal features of behavior. In order to suppress irrelevant, redundant and confusing video clips, a learnable contribution factor weighted LSTM was used to deeply extract the global long-time spatial-temporal features for abnormal behavior recognition. Through 3D-LCRN, abundant spatial-temporal features were extracted from both local to global and short-time to long-time levels. Experimental results show that the proposed method has excellent performance of abnormal behavior recognition in complex scenes such as illumination variation and background moving in comparison with the state-of-art methods.