Since Satoshi Nakamoto introduced Bitcoin, its popularity as an alternative method of payment has grown tremendously over the past few years. By the end of 2021, the market value of Bitcoin had exceeded $200 billion. One of the unique features of Bitcoin is its pseudonymous nature, as it is not directly linked to user identities like traditional usernames. This characteristic has led to misconceptions about Bitcoin being completely anonymous and has raised concerns about its potential misuse for untraceable transactions during illicit activities. Tracking Bitcoins associated with known addresses is usually manageable. However, it becomes challenging to trace Bitcoins when criminals utilize vague and ambiguous addresses to obfuscate their activities. Furthermore, the global usage of cryptocurrencies, including Bitcoin, continues to increase steadily, making it crucial to monitor Bitcoin transactions more carefully. Unfortunately, conventional methods have proven to be insufficient in effectively analyzing Bitcoin transactions. Therefore, this research focuses on the development of a Bitcoin transaction network (BTN) using pattern matching rules (PMR). Initially, the dataset undergoes preprocessing to identify missing symbols and unknown characters from the forensic blockchain dataset. Then, a Petri-Net model is applied to the pre-processed dataset, helping to identify properties such as timestamps, transaction IDs, work tera hash, and work error details. The Petri-Net model plays a significant role in parsing and constructing the BTN model. Subsequently, PMR conditions are formulated to extract transaction addresses along with their timestamp details. This allows PMR to detect illegal payment addresses by comparing them with known data, thereby identifying potential spam addresses. Additionally, a cache based PMR (CPMR) is applied to detect fraudulent transactions. CPMR stores all previously detected illegal payment addresses, allowing it to ignore those addresses during new transactions. This results in a reduction of fraud transaction detection time and speeds up the overall processing. The approach shows promise in enhancing the efficiency and accuracy of Bitcoin transaction analysis, addressing the challenges posed by the growing use of cryptocurrencies and the need for more robust forensic investigation methods.