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Abstract: At the Annual International Cryptology Conference in 2019(Crypto’19), Gohr introduced a deep learningbased cryptanalysis technique applicable to the reduced-round lightweight block ciphers with a short block of SPECK32/64. One significant challenge left unstudied by Gohr’s work is the implementation of key recovery attacks on large-state block ciphers based on deep learning. The purpose of this paper is to present an improved deep learningbased framework for recovering keys for large-state block ciphers. First, we propose a key bit sensitivity test(KBST) based on deep learning to divide the key space objectively. Second, we propose a new method for constructing neural distinguisher combinations to improve a deep learning-based key recovery framework for large-state block ciphers, further, we demonstrate its rationality and effectiveness through the perspective of cryptanalysis. Under the improved key recovery framework, we train an efficient neural distinguisher combination for each largestate member of SIMON and SPECK and finally carry out a practical key recovery attack on SIMON and SPECK large-state members. Furthermore, we propose that the 13-round SIMON64 attack is the most effective approach for practical key recovery to date. Noteworthly, this is the first attempt to propose deep learning-based practical key recovery attacks on 18-round SIMON128, 19-round SIMON128, 14-round SIMON96 and 14-round SIMON64. Additionally, we enhance the outcomes of the practical key recovery attack on SPECK large-state members, which amplifies the success rate of the key recovery attack in comparison to existing results.