As a newly discovered protein post-translational modification, lysine lactylation (Kla) plays a pivotal role in various cellular processes. High throughput mass spectrometry is the primary approach for detection of Kla sites. However, experimental approaches for identifying Kla sites are often time-consuming and labor-intensive when compared to computational methods. Therefore, it is desirable to develop a powerful tool for identifying Kla sites. For this purpose, we presented the first computational framework termed as DeepKla for Kla sites prediction in rice by combining supervised embedding layer, convolutional neural network, bidirectional gated recurrent units, and attention mechanism layer. Comprehensive experiment results demonstrated the excellent predictive power and robustness of DeepKla.