Neural Networks

SCI SCIE

国际简称：NEURAL NETWORKS 参考译名：神经网络

主要研究方向：工程技术-计算机：人工智能非预警期刊审稿周期：约12.0个月

《神经网络》(Neural Networks)是一本由Elsevier Ltd出版的以工程技术-计算机：人工智能为研究特色的国际期刊，发表该领域相关的原创研究文章、评论文章和综述文章，及时报道该领域相关理论、实践和应用学科的最新发现，旨在促进该学科领域科学信息的快速交流。该期刊是一本未开放期刊，近三年没有被列入预警名单。该期刊享有很高的科学声誉，影响因子不断增加，发行量也同样高。

2区中科院分区
Q1 JCR分区
1233 年发文量
7.2 IF影响因子
未开放 是否OA
128 H-index
1988 创刊年份
Monthly 出版周期
English 出版语言

Neural Networks is the archival journal of the world's three oldest neural modeling societies: the International Neural Network Society (INNS), the European Neural Network Society (ENNS), and the Japanese Neural Network Society (JNNS). A subscription to the journal is included with membership in each of these societies.

Neural Networks provides a forum for developing and nurturing an international community of scholars and practitioners who are interested in all aspects of neural networks and related approaches to computational intelligence. Neural Networks welcomes high quality submissions that contribute to the full range of neural networks research, from behavioral and brain modeling, learning algorithms, through mathematical and computational analyses, to engineering and technological applications of systems that significantly use neural network concepts and techniques. This uniquely broad range facilitates the cross-fertilization of ideas between biological and technological studies, and helps to foster the development of the interdisciplinary community that is interested in biologically-inspired computational intelligence. Accordingly, Neural Networks editorial board represents experts in fields including psychology, neurobiology, computer science, engineering, mathematics, and physics. The journal publishes articles, letters and reviews, as well as letters to the editor, editorials, current events, software surveys, and patent information. Articles are published in one of five sections: Cognitive Science, Neuroscience, Learning Systems, Mathematical and Computational Analysis, Engineering and Applications.

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Neural Networks期刊信息

ISSN：0893-6080
出版语言：English
是否OA：未开放
E-ISSN：1879-2782
出版地区：ENGLAND
是否预警：否
出版商：Elsevier Ltd
出版周期：Monthly
创刊时间：1988
开源占比：0.1207
Gold OA文章占比：13.28%
OA被引用占比：
出版国人文章占比：0
出版撤稿占比：
研究类文章占比：98.54%

Neural Networks CiteScore评价数据（2026年6月最新版）

CiteScore

SJR

SNIP

CiteScore 指数

10.9

1.545

1.978

学科类别	分区	排名	百分位
大类：Neuroscience 小类：Cognitive Neuroscience	Q1	9 / 129	93%
大类：Neuroscience 小类：Artificial Intelligence	Q1	85 / 570	85%

名词解释：CiteScore 是衡量期刊所发表文献的平均受引用次数，是在 Scopus 中衡量期刊影响力的另一个指标。当年CiteScore 的计算依据是期刊最近4年（含计算年度）的被引次数除以该期刊近四年发表的文献数。例如，2022年的 CiteScore 计算方法为：2022年的 CiteScore =2019-2022年收到的对2019-2022年发表的文件的引用数量÷2019-2022年发布的文献数量注：文献类型包括：文章、评论、会议论文、书籍章节和数据论文。

Neural Networks中科院评价数据

《新锐期刊分区表》（2026年3月发布）

Top期刊	综述期刊	大类学科		小类学科
是	否	计算机科学	2区	COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 计算机：人工智能 NEUROSCIENCES 神经科学	2区 2区

Neural Networks JCR评价数据

2025-2026年最新版

按JCI指标学科分区	收录子集	分区	排名	百分位
学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	SCIE	Q1	41 / 210	80.7
学科：NEUROSCIENCES	SCIE	Q1	37 / 330	88.9
学科：COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE	SCIE	Q1	32 / 210	85
学科：NEUROSCIENCES	SCIE	Q1	38 / 330	88.64

Neural Networks历年数据统计

影响因子

中科院分区

Neural Networks中国学者发文选摘

1、BCMDA: Bidirectional correlation maps domain adaptation for mixed domain semi-supervised medical image segmentatio
Author: Song, Bentao; Huang, Jun; Wang, Qingfeng

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108877
2、Passivity and synchronization of fractional-order coupled neural networks with multiple weights: A PD approac
Author: Li, Yu; Zhang, Xiulan; Zhang, Jiancheng; Er, Jianwei; Cao, Jinde; Liu, Heng

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108861
3、Fast sparse supervised learning framework with BLinex loss functio
Author: Jiang, Tiantian; Yu, Guolin; Ma, Jun

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108843
4、D3CNet: Long-term time series prediction based on dual decomposition and dual-channel hybrid networ
Author: He, Bo; Bo, Yunya; Li, Longbing

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108893
5、TFMPHGNN: Two-Fold multi-perspective heterogeneous graph neural network for sentiment analysi
Author: Agbesi, Victor Kwaku; Chen, Wenyu; Ejiyi, Chukwuebuka J.; Gosu, Gertrude Selase; Ukwuoma, Chiagoziem C.; Bamisile, Olusola

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108885
6、PSMEKL: Positional and structural multiple empirical kernel learning for node embeddin
Author: Zhu, Zonghai; Wei, Xinshuai; Xing, Huanlai; Feng, Li; Chen, De; Xu, Yuge

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108898
7、KSNet: Advancing autism prediction via KAN-based graph convolution and multi-source data fusio
Author: Wang, Chuang; Li, Dongyan; Sun, Sixiang; Liu, Junli; Li, Yuanyuan

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108892
8、Multi-modal contrastive learning based on molecular and textual data for drug response predictio
Author: Duan, Meiyu; Li, Xiaobo; Hou, Xiaodi; Qu, Yanchen; Cui, Hai; Zhang, Yijia

Journal: NEURAL NETWORKS. 2026; Vol. 201, Issue , pp. -. DOI: 10.1016/j.neunet.2026.108882