## 1 Introduction  
In recent years, the advent of web-based search engines has revolutionized the way people access information. These ubiquitous tools are extensively employed, not only for informational queries but also increasingly for learning purposes [1, 2]. Recognizing the potential of web-based search engines as valuable learning aids, researchers have focused on searching as learning (SAL), utilizing web-based search engines as a means to acquire knowledge and support learning processes and conceptualizing searching activities as learning activities [3, 4]. Unlike the traditional field of information retrieval, which primarily views searching as a tool for information acquisition, studies on SAL place greater emphasis on the learning process that learners engage in through web-based searching. Building upon this perspective, research in the domain of SAL focuses on the role of search systems in directly facilitating human learning [5]. This area of study goes beyond mere information retrieval to emphasize examining the effects, implications, and results derived from utilizing search systems in the context of educational processes. From a perspective of information retrieval, SAL research shifts the focus from the relevance of individual search results to supporting the learning process itself [6]. From an educational perspective, SAL research concentrates on deeply understanding how learners use search engines to meet their learning needs and how optimization can enhance learning outcomes [2]. Studies have proposed that SAL combined with thoughtful task planning can lead to enhanced learning outcomes [7; 8]. At the beginning of the SAL process, learners often only possess a vague understanding of the learning object, meaning their knowledge structures are insufficient to precisely articulate what they seek to learn. During the SAL process, learners are required to continually refine their learning tasks and retrieve relevant information from web-based search engine results, progressively constructing and refining their knowledge structures. This process involves the generation of queries, the evaluation of search results, and iterative adjustments and refinements of knowledge structures [5; 9]. Understanding and predicting learners' task planning ability in SAL are crucial for web-based search engine providers, recommendation systems, and educators [6; 10]. By comprehending learners' planning abilities, web-based search engines and recommendation systems can provide targeted guidance, suggest relevant learning resources, and optimize search results to facilitate effective learning [11]. Additionally, educators and instructional designers can utilize these insights to tailor instruction, provide appropriate scaffolding, and design interventions aimed at improving learners' planning skills, ultimately fostering metacognitive awareness and self-regulated learning [12]. To address the challenge of predicting learners' task planning ability, this paper proposes a novel method that leverages the Hierarchical Clustering Algorithm Based on Density Peaks (HCDP) model and the Tree-Structured Long Short-Term Memory Networks (Tree-LSTM) algorithm. The HCDP model is employed to capture and represent the hierarchical relationships among learning activities and learning subtasks. By modeling the learning process in this way, we can effectively capture the nuances of task planning in SAL. The Tree-LSTM algorithm is utilized to predict learners' SAL task planning ability based on the extracted features from the tree structure. The experimental results demonstrate the efficacy of our proposed method in effectively predicting learners' task planning ability within the context of SAL. Furthermore, the key features extracted from the tree structure serve as reliable indicators of learners' planning ability, providing valuable insights for web-based search engines, recommendation systems, and instructional designers.