Sequential recommendation involves suggesting subsequent items in a series of user activities. When recommending relevant items to users within the same account, the challenge lies in discerning diverse user behaviours to provide tailored recommendations based on individual preferences and timing. Cross‐domain sequential recommendation (CDSR) focuses on accurately extracting cross‐domain user preferences from both within‐sequence and between‐sequence interactions among items. Current approaches typically concentrate on learning preferences within a single domain using intra‐sequence item interactions, followed by a transfer module for cross‐domain preferences. However, this sequential process and implicit method are constrained by the effectiveness of the transfer module and may overlook inter‐sequence item associations. In this study, we propose an optimal cluster with attention‐based shared‐account cross‐domain sequential recommendation (O‐SCSR) system using deep reinforcement learning techniques. Our approach commences by formulating a modified hummingbird optimization (MHO) algorithm for clustering, effectively identifying latent users who share the same account to enhance the understanding of user interactions within shared‐account scenarios. Additionally, we design a domain filter based on quantum classic deep reinforcement learning (QCDRL), intelligently selecting interactions contributing to O‐SCSR. By quantifying rewards from transferred domain knowledge, the QCDRL‐based filter retains only valuable interactions for the task of SCDR. Finally, we validate the efficacy of our proposed O‐SCDR method using real‐world datasets, namely HVIDEO and HAMAZON. Through simulation results comparing the O‐SCDR system with existing state‐of‐the‐art systems, we demonstrate its effectiveness and legitimacy. [ABSTRACT FROM AUTHOR]