aspg-3-3717-1791417757 20261008000237 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2025 19 2 Ebtesam Hussain Almansor Computer Sciences Department, Applied College, Najran University, Najran 66462, Saudi Arabia As increasingly Arabic textual data becomes accessible through the Intranet and Internet services, there is an important requirement for technologies and devices to handle the related data. Named Entity Recognition (NER) is an Information Extraction task that became a major part of several other Natural Language Processing (NLP) tasks. NER for Arabic has been obtaining improving attention, but possibilities for development in performance are even accessible. In recent decades, the Arabic NER (ANER) task has been confined to great effort to increase its performance. The ANER difficult task is to collect vast corpora or immense white gazetteers/lists that address probably the majority of Arabic language challenges like complexity, orthography, and ambiguity. Recently, deep learning (DL) has been the most typically applied NER model in the Arabic language and others. DL methods utilize the features of words and text to identify NEs. This paper presents a Secretary Bird Optimization Algorithm for Enhancing Fusion Deep Learning in Arabic Named Entity Recognition (SBOFDL-ANER) model. The main intention of the SBOFDL-ANER technique is to develop an effective method for NER in Arabic text. At first, the text pre-processing stage is applied to clean and transform the raw text into a structured format for analysis. Next, the word embedding method has been implemented by the Word2Vec method. Besides, the proposed SBOFDL-ANER technique designs ensemble models such as deep belief network (DBN), elman recurrent neural network (ERNN), and multi-graph convolutional networks (MGCN) for the process of classification. Eventually, the secretary bird optimization algorithm (SBOA) implements the hyperparameter choice of ensemble models. A wide-ranging simulation was applied to verify the performance of the SBOFDL-ANER method. The experimental outcomes demonstrated that the SBOFDL-ANER model highlighted improvement over other existing methods 2025 379 391 3717 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.190227 https://www.americaspg.com/journal/3/article/3717 https://www.americaspg.com/storage/11742081101.pdf