In order to solve the problems that traditional video summarization methods often do not consider temporal information and the extracted video features are too complex and prone to overfitting, a video summarization generation model based on improved Bi-directional Long Short-Term Memory (BiLSTM) network was proposed. Firstly, the deep features of the video frames were extracted by Convolutional Neural Network (CNN), and in order to make the generated video summarization more diverse, the BiLSTM was adopted to convert the deep feature recognition task into the sequence feature annotation task of the video frames, so that the model was able to obtain more context information. Secondly, considering that the generated video summarization should be representative, the fusion of max pooling was adopted to reduce the feature dimension and highlight the key information to weaken the redundant information, so that the model was able to learn the representative features, and the reduction of the feature dimension also reduced the parameters required in the fully connected layer to avoid the overfitting problem. Finally, the importance scores of the video frames were predicted and converted into the shot scores, which was used to select the key shots to generate video summarization. Experimental results show that the improved video summarization model improves the accuracy of video summarization generation on two standard datasets TvSum and SumMe, its F1-score values are improved by 1.4 and 0.3 percentage points respectively compared with the existing Long Short-Term Memory (LSTM) network based video summarization model DPPLSTM (Determinantal Point Process Long Short-Term Memory).