随着静息态功能磁共振成像（resting-state functional magnetic resonance imaging, rs-fMRI）技术在脑连接性的广泛应用，越来越多的研究开始关注如何准确构建脑网络，并将其应用于脑疾病的分类中。归一化被认为是构建脑连接的一个重要步骤，可有助于消除信号幅值差异，但目前关于其在脑网络构建中的应用研究还相对有限。针对该问题提出了一种基于被试者归一化的脑连接方法，在统一被试者间信号幅值差异的同时又保留了脑区间幅值差异。此外，还引入熵来构建连接，通过比较多种交叉熵方法，评估了它们在脑疾病分类任务上的潜力，以期在分析脑连接上提供不同于相关性方法的新视角。实验结果表明：所提的被试归一化的交叉联合熵（cross joint entropy, CJE）具有较高的分类准确率，在精神分裂症（schizophrenia, SCZ）、轻度认知障碍（mild cognitive impairment, MCI）和自闭症谱系障碍（autism spectrum disorder, ASD）3种病症中，比传统归一化的相关性连接方法分别提升了约4%、6%和7%。

With the widespread utilization of resting-state functional magnetic resonance imaging(rs-fMRI) technology in exploring brain connectivity, an increasing number of studies have shifted their focus towards accurately constructing brain networks for the purpose of classifying brain diseases. Normalization is recognized as a pivotal step in the establishment of brain connections, serving to mitigate signal amplitude variations. However, current research on its application in brain network construction remains relatively limited. In order to solve this problem, a brain connectivity approach based on subject normalization was proposed, aiming to standardize signal amplitude differences between subjects while preserving amplitude distinctions between brain regions. Furthermore, entropy-based methods for network construction were introduced and evaluates their efficacy in brain disease classification tasks by comparing various cross-entropy methodologies. The objective was to offer a novel analytical perspective distinct from conventional correlation-based approaches in studying brain connectivity. Experimental findings demonstrate that the proposed subject-normalized cross joint entropy(CJE) method achieves notable classification accuracy in schizophrenia(SCZ), mild cognitive impairment(MCI), and autism spectrum disorder(ASD), exhibiting enhancements of approximately 4%, 6%, and 7% respectively compared to traditional normalized correlation-based methods.