With the widespread adoption of big data technology, the diversity of data sources is continuously evolving. Data service technology is a technology derived from providing effective data interfaces for data applications. Based on the adaptive deep learning algorithm, this study proposes an improved service plan. First, the problem of randomly selecting the sending location of the data packet in the asynchronous random service scheme was analyzed, which leads to the waste of channel resources. Then, combined with the adaptive deep learning algorithm, an adaptive service scheme is specified. For the problem of large delay, the data frame is divided into multiple uniform position intervals. Therefore, the user learns the position interval until the user tends to select a position within the fixed position interval to send the data packet. Furthermore, in the algorithm’s iterative process, adaptive deep learning was usedbased on the ability of the algorithm to perform intensive learning. A detailed analysis of the various scenarios, the essential mode of the technology, and the computer simulation of the throughput and packet loss rate indicators of the proposed scheme in the three environments are provided to demonstrate the superiority of the proposed scheme.
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