A lens is installed into the laser before leaving the factory to ensure focusing. However, the laser beam is particularly sensitive to focusing and can easily become out of focus, with the machine shaking during the focusing process, further complicating the focusing. To solve this problem, an image sequence prediction model PCLT-Net (PMA-ConvNeXt-LSTM-TA), which combines a convolutional neural network with parallel multi-attention (PMA), a long shortterm memory (LSTM), and temporal attention mechanism (TA),was built for real-time prediction of the in-focus or out-of-focus state of laser beams. First, a convolutional neural network was used to extract the spatial features of each frame. Then, a recurrent neural network was used to learn the temporal dependencies, and a temporal attention mechanism was used to highlight the key frames. Finally, the focus state of the laser beam was predicted using a classifier. In the experimental results, this method achieved 90% accuracy in the real-time laser focusing state prediction task, significantly improving the quality and production efficiency of laser products.