<p>For decades, goal modeling has been significant in the early stages of requirements engineering. Numerous studies have demonstrated the effectiveness and practicality of utilizing requirement goal models. However, creating goal models is usually done manually, which is a challenging task for large-scale systems. Our research objective is to simplify and optimize the creation process of iStar goal models with a semi-automatic framework, assisting in the practical adoption of goal modeling approaches. In this paper, we significantly extend our previous work to better deal with the above problem. Specifically, we utilize the Design Science Research paradigm to propose an interactive and iterative modeling process that combines human decision-making steps with automated analysis steps, minimizing modeling costs while ensuring the quality of the models. This research is based on conducting interviews on the practicality of iStar modeling and conducting a literature review on the automation of iStar modeling for goal modeling. To fulfill the modeling process, we propose a novel hybrid approach that features highly customizable logical reasoning rules and deep learning techniques, allowing for tailored selection and design according to specific needs. To pragmatically promote our approach, we develop a prototype tool with a user-friendly interface. In this paper, we select BERT as the deep learning component and design a series of rules based on BERT as an example. Then, an evaluation is conducted using this specific implementation of this approach. Besides, we conduct a case study on a real-life scenario to evaluate the effectiveness of our modeling approach, showing the efficiency of the modeling process. These results indicate that our proposal efficiently establishes high-quality goal models and thus pragmatically contributes to adopting goal model analysis approaches.</p>