Abstract: To address the issue of shape prediction in the roller quenching process, this paper proposes a novel method based on clustering analysis and an improved Bidirectional Gated Recurrent Unit(BiGRU) network. Key process parameters influencing plate shape during quenching are first analyzed, and K-means clustering is applied to categorize sample data, thereby identifying the distribution characteristics of different steel grades and specifications. For continuously produced steel plate batches, a BiGRU model is employed to extract high-dimensional temporal features, capturing the sequential dependencies between preceding and succeeding shape variations. By integrating Convolutional Neural Networks with bidirectional gated units, an enhanced BiGRU model is constructed for plate shape prediction. Experimental results demonstrate that the K-means-guided improved BiGRU model achieves accurate shape predictions within acceptable process error margins. This method provides a reliable foundation for plate shape control in steel quenching processes and contributes to intelligent control in advanced manufacturing.