A texture sequence feature combining texture statistical characteristics with time series is proposed for rapid prediction of the state of laser cladding deposited layer. Texture features in the molten pool images are extracted using high-order dense texture descriptor operators and transformed into probability sequences with the aid of multi-cell histograms. Texture features are screened based on the types of points of interest to improve the sequence, enhancing the prediction accuracy and speed for deposited layer. For different types of points of interest, the optimal texture sequence obtained achieves an improvement of more than 6% in prediction accuracy for deposited layer state compared to the full-texture sequence. The proposed texture sequence features, when compared with molten pool image features, exhibit an average improvement in prediction speed of more than 17 times across different deep learning models. Overall, the results indicate that this method significantly improves prediction speed while maintaining accuracy, satisfying the requirements of high-speed monitoring in laser cladding.