## 1 Introduction  
Rich communication suite (RCS) messages, known as the the evolution of Short Message Service (SMS) messages, are transmitted between mobile devices built with RCS features through 5G communication infrastructure. It is expected to expedite the 5G ecosystem by way of its RCS business messaging feature, known as A2P (Application to Person) messaging, to proper palm devices for a secure, multimedia and interactive user experience. Currently, according to the 5G Message White Paper [1], many telecommunications providers have released their 5G RCS messaging solutions and numerous RCS build-in peptides are under development. Fundamentally, the RCS technology is a comprehensive upgrade of Short Message Service in terms of data, functions, features, experiences and services. An RCS message is a rich media message that is not only full of various types of data, such as texts, audios, pictures and videos, but also full of comprehensive functions, including group chatting, map navigation, calendar, e-Payment systems and applets. The user experience is greatly improved by phone number entry, security and easy accessibility. The faster reading rate, feedback rate and action rate enhance user loyalty and stickiness, which benefit value-added services and e-commercial cooperation. In fact, it is foreseeable that RCS messaging will create a new ecosystem of 5G technologies, and apparently a great deal of revenue is growing dramatically. A study released by the Global System for Mobile Communications Association (GSMA) in 2019 shows that 1.4 billion 5G devices will account for 15% of the total mobile industry globally by the year 2025 [2]. According to the white paper released by the China Academy for Information and Communications Technology (CAICT) [3], 5G devices will increase to 816 million in China by the year 2025, which will account for 48% of mobile devices nationally. The number will continue to increase to 1.5 billion and account for 85% by the year 2030. In addition, by the year 2025, the volume of RCS messages is predicted to reach 3501.2 billion and its revenue will increase to 53 billion dollars in China. However, in every challenge lies a greater opportunity. Although spam RCS messages will most probably appear in the age of 5G, the companies providing 5G telecommunication services have established research projects to filter spam RCS messages. Although the comprehensive RCS message data led to the complex filtering method, hundreds of fields of the data are adequate and benefit the filtering accuracy. In this research, a combination method of a hidden Markov model and convolutional neural networks is proposed to filter RCS messages based on the text data of RCS messages. We first choose 210 text fields possibly containing spam information from RCS interface specifications, label them 'ham' (not spam) and'spam' (illegal text) for short text data fields, then interpret this information with proper spam-distinguishable weights with the hidden Markov model (HMM) and finally identify spam RCS messages with the convolutional neural network (CNN) method. The main contributions of this research are threefold:  
* This paper is the first time a method for RCS message filtering has been proposed. * The proposed method is a combination of HMM and CNN methods and produces the RCS message property based on 210 text fields where spam information may exist. * The proposed combination method achieves promising results. The rest of the paper is organized as follows: Section 2 discusses related research, such as spam filtering methods and machine learning models. Section 3 proposes the combination method of HMM and CNN models. In detail, Section 3 first points out the 210 text fields and then discusses the HMM responsible for weighting and labeling short text with ham or spam.