Simulation of incremental update of electronic document information based on big data technology

Experimental results show that the algorithm converges faster and has a smaller average error compared to the BP algorithm and the attribute reduction algorithm. The algorithm converges in 100 iterations with the addition of 40 neurons. With the development of information technology, the volume of electronic document data has increased dramatically and the content is diverse, if not updated in a timely and incremental manner, the electronic document information will lag behind and fail to meet the actual needs. Traditional algorithms need to recalculate a large amount of existing data when dealing with large-scale new data, resulting in long processing time and high resource consumption, which affects the accuracy and timeliness of information. To address this issue, this research investigates related incremental update models based on big data technology and deep learning algorithms. The feedforward neural network can learn data features quickly and is suitable for incremental update model construction. Deep algorithms in incremental updating of electronic documents can automatically extract deep and abstract features from a large amount of complex data. On the basis of the feedforward neural network model, the network structure and parameters are optimized to enhance the algorithm’s learning capacity for the information characteristics of electronic documents. Through first-order approximation, the number of iterations in model parameter updates is significantly reduced, thereby improving the computational efficiency of the algorithm. The network structure optimization is mainly achieved by increasing the number of neurons in the hidden layer, and the BP algorithm and gradient descent method are used to iteratively calculate until the model converges, so as to improve the model’s ability to capture the dynamic features of incremental data. Experimental results show that the algorithm converges faster and has a smaller average error compared to the BP algorithm and the attribute reduction algorithm. The algorithm converges in 100 iterations with the addition of 40 neurons. Consequently, it is suitable for the incremental update of electronic file information.