There's a number of positions outlined in your SEP link to Searle's Room that make clear that philosophy has not decided by consensus one way or another the question of human and semantic understanding. The history of AI is an ongoing debate, in fact, about the question. A great introduction into that history is Nils Nilsson's The Quest for Artificial Intelligence. I'll caution you that anyone who answers you strongly negatively or in the affirmative hasn't even picked up and read this book. Philosophy is undecided largely because philosophy has reached no strong consensus on what constitutes understanding. Science has not reached a consensus on semantics in the brain. That being said, computers have made gains in the last couple of decades, perhaps not demonstrative of human-level intelligence, but certainly enough to listen to you and fulfill some of your needs. Essentially, though, besides agnostics, there are two camps: those who believe in a Cartesian notion of understanding that rejects anything other than humans as being capable of human-esque intelligence, and an upstart crowd that is interested in artificial general intelligence and believes it is possible in theory. (Warning that my bias is the latter.) Whatever the case, you are firmly in the philosophy of artificial intelligence, a relatively new branch of philosophical inquiry less than 100 years old given the emergence of digital computation in the late 1930's and early 1940's.
Getting Your Feet Wet
The dream to animate the inanimate to conduct itself as a human goes back thousands of years straight into the mythology of the Proto-Indo-Europeans. The abridged accounting thereof also seems to be an intro in every book today that seeks to introduce AI. Obviously, you have two questions at play, one about Searle and the Chinese Room, but the larger issue of what does philosophy say about developing machines that think.
You've cited the Chinese Room Argument, which has a number of posts on this site. Start with a review of those:
Understanding Searle's argument and the arguments that reply to it, particular the systems response are necessary for orienting yourself. Once you've done so, if I were you, I'd reach out and get a copy of The Philosophy of Artificial Intelligence by Margaret Boden and What Computer Cant Do by Hubert Dreyfus. If you want to know see what the AGI camp is cooking up, a good recent publication called Artificial General Intelligence by Ben Goertzel and Cassio Pennachin (Eds.) offers into some of the (IMNSHO) flailing attempts to create architectures to imbue software with human-level intelligence traits.
As to the question of how can Searle be so sure? Well, John Searle is renowned for his philosophy, and that confidence may be a function of his success as a philosopher and his lack of technical sophistication as a computer scientist. John Searle's continuation of the successes of the linguistic turn in philosophy is tough to dispute. He's written a lot about how language reflects on reality, both personal and social, but I would point out that Searle has a tool he uses to deal with the complexity of the mind called The Background. He often dismisses details right into that nebulous, diffuse thing to simplify to make his points. Overall, it's an excellent strategy for narrowing down his argumentation to what deserves focus, but the downside to that is that runs the danger of making it too easy to sweep aside relative propositions since informal argument is governed by non-monotonic logic and defeasible propositions.
Human-Level Intelligence and the Nature of Thinking
The other part of your question revolves around coming to terms with just what it means to simulate understanding, particularly of language. As you are likely aware, Alan Turing is famous for many things, but among them is his Turing Test which is an attempt to operationalize human semantic intelligence. As we approach 100 years, no one has been able to do it, which in the history of artificial intelligence has often been touted as just around the corner much as fusion reactors have been constantly 30 years (Discovery Magazine) away. In fact, when Hubert Dreyfus began attacking the AI program on campus and with RAND, he noted the outright hostility of his fellow thinkers almost immediately.
Why has the promise of AI been so slow to materialize (though gains in machine intelligence have accomplished some fantastic goals lately)? Well, it boils down to what the science of linguistics has discovered about semantics. The easiest way to explain it is to say that meaning is rooted in physical embodiment, and that processing strings in a serial ALU falls short of some of the connectist nature of the human brain. These are the computational details where the question of human intelligence gets gritty and where an ignorance of the materials science and the mathematical structures of computation start to have a bearing on the philosophy of mind.
In fact, the question of what constitutes human intelligence is not only an open question in the philosophy of mind, but in psychology itself, where there are two, roughly speaking, models vying for approval, the Cattell-Horn model which is related to the G factor and is operationalized through intelligence quotient testing, and what might be called a pluralistic notion of intelligence that is most famous through Howard Gardner's MI theory which is popular with humanists and educators. As there are adherents to harder and softer "sciences of the mind", so too does this bias reflect itself in the notion of intelligence.
Semantics and Understanding
Ultimately, the question you are after is rooted more in the philosophy of language more than anything else, because the discussion of the syntax-semantics dichotomy rests there with the philosophers and scientists of language. There are a number of competing models for how exactly this stuff, thing, experience called "meaning" happens, and if you really want to understand what's involved in how semantics functions with people, I'd recommend two books to start you on your way, though they aren't easy reads. First, Ray Jackendoff has his Foundations of Language which is highly technical, but makes a specific architectural argument about how embodied systems of the brain give rise to what we recognize as meaning. The second is also a tough read, but worthwhile if you really want to understand why the promise of human-level AI and language use has failed to materialize, Cognitive Linguistics by Evans and Green which offers a comprehensive picture of how language and meaning are grounded in bodily experience.
Now, what I've offered here isn't an easy answer so much as a blueprint for understanding why most philosophers are out of their element when discussing how to implement an aspect of actual human cognition rooted in neural computation
on systems designed to implement the von Neumann architecture of a Turing machine. Searle's contributions to language, semantics, and intentionality are indisputable, however, in some regards, the question of engineering semantic understanding has begun to move out of philosophy and into the scientific domains of machine learning, software engineering, and neurology. As such, you will see resistance to abandoning classical notions in philosophy like truth-conditional semantics and Platonic mathematics that are adduced in favor of transcendental forms of metaphysics. In fact, Searle himself concedes the brain is a biological computer, but just remains skeptical that our current computer technology can mimic them, which is a measured conservatism.