Intelligent Academic Paper Analysis System Based on Large Language Models: Practice in Automated Scientific Literature Processing

This article introduces an open-source project named "Intelligent Research Paper Analyzer", which uses large language models to automatically analyze academic paper PDFs and address the inefficiency of traditional literature management. Core features include converting unstructured documents into structured data, extracting metadata and technical keywords, and providing keyword search functionality, offering researchers a practical auxiliary tool for literature processing.

In scientific research, researchers need to read a large number of papers, but traditional literature management methods have efficiency issues: the unstructured nature of PDFs makes information retrieval difficult, and manual extraction of metadata (title, authors, etc.) is time-consuming and labor-intensive. To address these problems, this project was developed as the final project for the CSC 7644 course (Applied Large Language Model Development), aiming to build a scientific research auxiliary tool using LLM technology.

The system adopts a modular design and follows the data flow-driven approach, forming a closed loop from PDF input to structured output. Core components include: PDF processing module (text extraction), metadata extraction module (LLM intelligent recognition), keyword generation module (semantic understanding to generate professional terms), processing pipeline (coordinating batch processing), search engine (keyword retrieval and sorting), and output manager (saving in JSONL/Excel formats).

1. PDF Text Extraction: Use the PyPDF library to process papers in different formats, with a fault-tolerance mechanism designed to handle parsing issues; 2. Metadata Extraction: Leverage LLM's semantic understanding ability to identify metadata such as title, authors, and journals, without the need for custom rules, ensuring strong universality; 3. Keyword Generation: Generate professional terms based on semantics, supporting quick topic understanding and literature indexing; 4. Search Sorting: Adopt a keyword overlap scoring mechanism to provide effective relevance sorting.

Tech Stack: Python 3.10+, dependencies include OpenAI/OpenRouter API (supports switching between multiple providers), PyPDF, Pandas, python-dotenv, etc. Usage Flow: 1. Place PDFs in the data directory; 2. Configure API keys; 3. Run the main program to build the database; 4. Obtain JSONL/Excel outputs. Keyword search and result export are supported.

Application Value: Build a searchable structured literature library for researchers; assist in batch processing of papers to support review writing; serve as a teaching case for LLM application development. Limitations: Parsing issues with some PDF formats, LLM outputs requiring manual verification, and simple scoring for keyword matching. Improvement Directions: Introduce more powerful PDF parsing libraries, add LLM output validation, and explore semantic search as an alternative to keyword matching.

This project demonstrates the application potential of LLMs in scientific research assistance. By combining LLMs with traditional technologies, a practical tool is built with a small amount of code. Its modular architecture, pipeline design, and multi-format output provide a template for similar applications; support for multiple LLM providers reflects the "provider-agnostic" design concept. For LLM application developers, it is an excellent reference project with clear code and practical functions.