Aiming at the limitation that deep long and short-term memory network(DLSTM) algorithm cannot perform parallel computing and cannot obtain global information, in this paper, feature extraction and feature processing are firstly carried out according to the characteristics of eye movement data and tracking data, then by introducing a convolutional neural network (CNN) into a deep long and short-term memory network, developed a new network structure and designed a fusion strategy, an eye tracking data fusion algorithm based on long and short-term memory network is proposed. The experimental results show that compared with the two fusion algorithms based on deep learning, the algorithm proposed in this paper performs well in terms of fusion quality.

翻译：鉴于深长和短期内存网络(DLSTM)算法无法进行平行计算和无法获得全球信息这一局限性,在本文件中,特征提取和特征处理首先根据眼睛移动数据和跟踪数据的特点进行,然后将一个革命神经网络引入一个深长和短期的内存网络,开发了新的网络结构并设计了一个聚合战略,提出了基于长期和短期内存网络的眼跟踪数据聚合算法,实验结果显示,与基于深层学习的两种聚合算法相比,本文件中提议的算法在融合质量方面表现良好。