Abstract: A diesel engine, the power source of a tractor, most directly determines the performance and safety of the tractor. Many efforts have been made on the faults of diesel engines in the agricultural field, due mainly to the complexity of the mechanism, diversity of faults, and the concurrency of multiple faults. Furthermore, fault diagnosis of diesel engines is developing towards artificial intelligence in recent years. Among them, back propagation (BP) neural network with excellent non-linear mapping has widely been used in fault diagnosis of tractor diesel engines. However, BP neural network tends to fall into local the minimum and slow convergence in engineering practical application. In this study, a modified fault diagnosis model was proposed for the tractor diesel engine using Linear Weight Decrease-Quantum Particle Swarm Optimization-Self Organizing Maps Back Propagation (LWD-QPSO-SOMBP) neural network. Firstly, A Self Organizing Maps (SOM) neural network was used to process the input data of the BP neural network. A composite network model was proposed to combine the SOM and BP neural network, in order to alleviate the training pressure of the BP neural network. Secondly, the network structure was modified to optimize the initial network weights, where the Linear Weight Decrease-Quantum Particle Swarm Optimization (LWD-QPSO) was proposed for the network weights and thresholds. Thirdly, the failure mechanism of the tractor diesel engine was analyzed to determine 8 kinds of data signals for the failure. Finally, the structure parameters were determined for the LWD-QPSO-SOMBP neural network model. A fault diagnosis test was then carried out using Controller Area Network (CAN) bus technology. The CAN bus was used to collect and analyze the sensor signal data of the Weichai WP6 tractor diesel engine, thereby evaluating the performance of the LWD-QPSO-SOMBP neural network. A comparison was also made on several neural networks to verify the accuracy of fault diagnosis and performance of LWD-QPSO-SOMBP neural network, including BP, Self Organizing Maps Back Propagation (SOMBP), Particle Swarm Optimization-Self Organizing Maps Back Propagation (PSO-SOMBP), and Linear Weight Decrease-Particle Swarm Optimization-Self Organizing Maps Back Propagation (LWD-PSO-SOMBP), and SOMBP neural network optimized by Improved Quantum Particle Swarm Optimization (IQPSO). The test results show that the LWD-QPSO-SOMBP neural network effectively integrated the SOM neural network in data preprocessing and the PSO in optimizing the initial weight threshold of the BP neural network, compared with the rest. As such, a high-precision fault diagnosis of tractor diesel engines was thus achieved. The LWD-QPSO-SOMBP neural network greatly improved the convergence rate of the framework using the SOM neural network to pre-process the network input data. The iteration times were reduced 97.40% from 2431 to 63, compared with single BP neural network. At the same time, LWD-QPSO was adopted in the LWD-QPSO-SOMBP neural network to optimize the initial weight threshold of the network. It was found that the particle fitness of traditional PSO was reduced greatly further to improve the convergence accuracy and speed of the network. The PSO particle fitness decreased by 26.67% from 0.15 to 0.11, while, the convergence error of the network decreased by 85.00% from 0.004 to 0.0006, compared with the traditional. The diagnostic accuracy of the LWD-QPSO-SOMBP neural network model was greatly improved, while, the training accuracy increased from 85.00% to 99.44%, compared with the single BP network. Consequently, the LWD-QPSO-SOMBP neural network model presented an excellent diagnostic performance. This finding can provide a sound reference for high-precision intelligent fault diagnosis of tractor diesel engines.