Journal assessment indicators have been widely investigated to improve the journal ranking and classification. However, most of the previous studies mainly focused on citation related indicators, while the authorship side was rarely explored. This paper studies the mechanism between the citation and authorship related indicators and defines new concepts of output factor and input factor. A framework of two-dimensional journal classification method is developed by combining the output and input factors together through comprehensive weighting methods. A two-dimensional journal performance value (TJPV) is defined to measure the performance of journals in a two-dimensional typology. A journal performance criterion is defined to divide the journals into four different levels according to the TJPV equipotential arcs. The journals in the plane are also classified into three groups, i.e., Input Factor-Oriented ones, Balanced ones, and Output Factor-Oriented ones. A balance criterion is developed to determine whether these journals are uniformly distributed or not. The ranking method based on TJPV performance criterion is only applicable for uniformly distributed journals. Thus, a Pareto non-dominated set-based sorting method is proposed for addressing the scenario of unevenly distributed journals. To demonstrate the effectiveness and validity of the proposed method, the data of output and input factors from 84 initially selected journals in “Operation Research & Management Science” category are collected to conduct a case study. New insights are obtained which are helpful to guiding the development of future journal classification criteria.

期刊评估指标已得到广泛研究，以提高期刊的排名和分类。但是，以前的大多数研究主要集中在与引文相关的指标上，而关于作者身份的方面却很少被探讨。本文研究了引文和作者相关指标之间的机制，并定义了输出因子和输入因子的新概念。通过综合加权法，将输出因子和输入因子结合在一起，建立了二维期刊分类方法的框架。定义了二维日记帐性能值（TJPV）以衡量二维类型学中的日记帐性能。定义了轴颈性能标准，以根据TJPV等电位弧将轴颈分为四个不同的级别。平面中的日记帐也分为三类，即面向输入因子的日记帐，平衡日记帐和面向输出因子的日记帐。建立平衡标准来确定这些日记帐是否均匀分布。基于TJPV绩效标准的排名方法仅适用于均匀分布的期刊。因此，提出了一种基于帕累托非支配集的排序方法，以解决期刊分布不均的情况。为了证明该方法的有效性和有效性，收集了来自“运营研究与管理科学”类别的84种最初选择的期刊的输出和输入因子的数据，以进行案例研究。获得了新的见解，它们有助于指导未来期刊分类标准的发展。