## 1 Introduction  
With the rise of large-scale knowledge graphs [1], such as Wikidata [2], DBpedia [3], Freebase [4], etc., users are finding it difficult to quickly obtain a large amount of valuable knowledge from them due to their large volume and complex data structure. Knowledge graph question-answering (KGQA) has emerged as a critical tool for addressing this challenge and has garnered considerable attention. When compared to traditional question-answering tasks, KGQA improves semantic understanding and answer accuracy [5]. As shown in Table 1, in this paper: (1) Simple questions containing one topic entity with one relation [6, 7] and multi-hop questions containing one topic entity with multiple relations [8] are collectively referred to as simple questions. (2) Aggregate complex questions [9] containing multiple topic entities with multiple relations are referred to as complex questions. Many studies have been published by researchers to improve simple question answering over knowledge graph accuracy. Existing studies have achieved an accuracy rate of up to 78.1% [10] on SimpleQuestions [11], a dataset of simple questions representing single-hop relations. However, due to the complexity of entities and relations, there is still a lot of research space and application value for complex questions, and the main challenges we face are as follows. 1. Complex question has multi-topic entities and multi-hop relations, which present difficulties for neural networks to learn directly. 2. Complex question components contain various constraint information such as time, space, numerical operations, etc., which can interfere with the downstream reasoning model. 3. The knowledge graph is large and the question may not contain all path information, the task of identifying a graph's locating number is a non-deterministic polynomial-time hard problem (NP) [12]. However, the model needs to quickly identify the optimal path from it and make reasonable inferences about the missing path information. To summarize, we design a solution for complex question-answering tasks over knowledge graphs and decompose them into three tasks by simulating how humans solve complex questions, question decomposition, constraint extraction, and question reasoning. In the question decomposition section, we develop a model called BTAM to address the problem that complex questions are difficult to train directly in neural networks by using a pre-trained model as an encoder to encode the semantic information of the question and designing a dependency encoder to encode the syntactic structure between words in the question. The model can better represent complex questions. In the constraint extraction section, we propose a constraint representations method and design a model that, while extracting constraint information also determines the subject on which the constraint information acts via dependency analysis, potentially increasing the question reasoning accuracy rate. In the question reasoning section, we present the model HBHR, which uses beam search for iterative reasoning in the knowledge graph based on the information derived from the first two sections, and can improve the accuracy rate while reducing the search space, to solve the problem whereby the optimal path cannot be found quickly due to the excessively large size of the knowledge graph and the missing path information cannot make a reasonable inference. Furthermore, for similarity detection, the model employs a pseudo-siamese network, which engages different encoders to encode the question and paths for computation, and the encoders are designed in a learnable form to obtain a better vector representation during training, thereby improving the reasoning process interpretability and missing path information exploration. The question-answering model for complex questions proposed in this paper uses a pipeline structure with interpretability and a neural network model for information extraction, which reduces data quality requirements and renders the model more parsable, accurate, and robust.